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Generative AI System Planning Framework 🤖

A well-structured approach is essential when planning an AI system, balancing high-level conceptualization with practical implementation details. Creating a high-level roadmap for an AI system is a crucial step in project planning. This process begins with a critical first step: clearly defining your system's goals, inputs, and outputs. This foundation will guide all subsequent development decisions and help ensure your AI system effectively addresses its intended purpose. It's important to understand that this planning process is inherently iterative. In your first attempt, you'll likely encounter many unanswered questions and gaps in your understanding. This is normal and expected. The key is not to get bogged down by these uncertainties initially. Instead, focus on completing the following steps, even if there are gaps in your thought process. Your initial roadmap will be imperfect, but it will provide a good starting point. The idea is to iterate over the planning phase multiple times. With each pass, your understanding of the problem will get better, and your design will improve. You'll start with a broad structure, intentionally ignoring gaps and unanswered questions at first. The important part is to try creating basic plan despite your uncertainties. When you feel stuck, focus on what you already know and can act upon. Keep moving forward with the aspects of your plan that are clear and defined. This approach maintains momentum and often leads to insights about the less certain parts of your project. As you work, keep a separate list of uncertainties, unknowns, and areas that need further investigation. This allows you to track uncertainties without letting them halt your progress. Regularly review and update this list as you gain new information. Also, consider temporarily reducing the scope of your project or outcome. Focus on creating a simplified version that captures the core essence of what you're trying to achieve. This "minimum viable product" approach allows you to make tangible progress and gain valuable insights. As you complete this scaled-down version, you'll develop a better understanding of the project's complexities and challenges. From there, you can gradually expand the scope, adding more components or advanced features in a controlled, iterative manner. Each iteration allows you to fill in more details, address previously identified gaps, and incorporate new insights. Step 1: Define the Goal, Input, and Output Start by clearly articulating the main objective of your AI system, including the broad input it will process and the output it will generate. This step sets the foundation for your entire project. Defining the Goal: What the AI system will accomplish. When crafting your goal, ensure it's specific, actionable, and aligned with the overall problem you're trying to solve. A well-structured goal outlines the system's purpose or action and states its intended outcome. Consider the problem you're solving, who you're solving it for, and any key constraints. Here is a template to write the goal of your AI system: Create an AI system that [performs specific function] on [inputs] and produces [output]. Defining the Input: What information the AI system will need to achieve that goal. This process involves determining what data is relevant, and available. Input data may be structured (e.g., spreadsheets, databases, CSV files) or unstructured (e.g., PDFs, word documents, emails, websites, customer chats). Many times, you may need to extract features from the raw data and it can be one of the steps in your AI system plan. At this stage, you don't have to think about the needed feature engineering details, rather the scope is limited to understanding what data is needed, where it will come from and ensuring it's accessible. Your identified inputs should comprehensively cover all the AI system needs to perform its intended function effectively. Defining the Output: What the outcome of the AI system is–outcome content and format This includes determining the output format, deciding on the required level of detail, and considering how end-users will utilize the information. Plan for result interpretation and explanation, ensuring the output is actionable and understandable. The output definition should align closely with the system's goals and user needs, providing clear, relevant, and valuable information that directly addresses the problem the AI system is designed to solve. Stakeholder Involvement: Throughout this definition process, it's crucial to involve relevant stakeholders. This may include end-users, domain experts, managers, and other key decision-makers. Their input helps ensure that the system's goals, inputs, and outputs align with real-world needs and constraints. Stakeholders can provide valuable insights into: 1. The specific problems the AI system should address 2. The types of data available and any limitations in data access 3. The most useful forms of output for decision-making processes 4. Potential challenges or considerations in implementing the system By involving stakeholders early in the planning process, you will build something useful, avoid rework, and provide better ROI. This initial planning stage sets the foundation for your entire AI project. By clearly defining your goals, inputs, and outputs—with input from key stakeholders—you create a solid framework that will guide your development process and help ensure your AI system meets its intended objectives. Let's walk through a practical example of how to apply the principles discussed in this document. We'll consider a scenario where you've been tasked with creating a Generative AI system that takes a Microsoft Word document as input and generates a PowerPoint presentation from it. After consulting with your manager and relevant stakeholders, you've developed the following: Goal: Create an AI system that create powerpoint slides for a given book chapter Input: A book chapter in MS Word format. Output: Powerpoint slide for this book chapter Step 2: Identify main steps This process involves identifying the main steps that will guide your project from inception to completion. These steps should represent the major phases or milestones in your system's operation, providing a framework for more detailed planning later. There are a few effective approaches to identifying these steps, each offering unique advantages. Back-to-start : One method is to begin with the end goal in mind and work backwards. Visualize what your completed AI system should accomplish, then reverse-engineer the process to identify the major components or processes needed to reach that objective. As you do this, consider the logical sequence of these steps. Ask yourself: What needs to happen first? Which steps depend on the completion of others? Component Listing and Ordering : A second strategy involves listing out all potential components that you think may be needed, ordering them logically, and then refining the list by keeping or dropping components as needed. This more flexible, brainstorming-oriented approach can bring out creative solutions and help identify parallel processes or steps that don't necessarily fit into a strict linear sequence. Gap analysis : Another valuable approach is to analyze the gap between the steps identified so far, or between the latest identified step and the input. This gap analysis method can help uncover missing intermediate steps, ensure a logical flow from input to output, and reveal potential challenges or complexities that weren't immediately apparent. In practice, a combination of these approaches often yields the most robust and comprehensive planning process. By viewing the problem from different angles, you can potentially develop more innovative and effective solutions. Regardless of the method used, it's crucial to note that at this stage, you should not worry about how to implement these components or processes. The focus is solely on identifying what steps need to happen, not on determining how to make those steps happen. Implementation details will come later in the planning process. For now, concentrate on creating a high-level overview of the necessary stages in your AI system's development. Continuing with the example, let's walk through the thought process of deriving our main steps: Starting Point: Generate a PowerPoint Presentation We begin by considering our end goal: a PowerPoint presentation generated from a Word document. Question: What's the last step needed to achieve this? Answer: We need a step that takes finalized slide content and creates a PPT file. Step Identified: generatePPT Generating Slide Content Before we can generate the PPT, we need the content for each slide. Initial Thought: Can we simply feed the entire document to an LLM and ask it to generate slides? Problem Identified: This approach could be too complex for the LLM due to the "lost in the middle" problem with long contexts. Solution: Break down the task into smaller, more manageable steps. Breaking Down the Content Generation Task How can we make the task easier for the LLM? Idea: Instead of processing the whole document at once, we can work with smaller segments. Question: How do we determine these segments? Answer: We can use the document's structure, specifically its main topics. Identifying Document Structure Before we can segment the content, we need to understand its structure. Step Identified: extractTopics This step will analyze the document to identify main topics and their hierarchy. Segmenting Content Once we have the topics, we can divide the document content accordingly. Step Identified: extractContentSegment This step will associate relevant content with each identified topic. Generating Slide Content for Each Topic Now we can generate slide content more effectively, focusing on one topic at a time. Step Identified: generateSlideContent This approach allows the LLM to work with smaller, more focused chunks of information, potentially improving accuracy and relevance. Initial Data Extraction Realizing we need the document content before any processing can occur. Question: What's the very first step in our process? Answer: We need to extract the content from the Word document. Step Identified: extractContentFromWordDoc By walking through this thought process, we've arrived at a logical sequence of steps: extractContentFromWordDoc extractTopics extractContentSegment generateSlideContent generatePPT Step 3: Identify Inputs, Outputs, and Repetition for Each Step After identifying the main steps in your AI system, the next crucial task is to determine the inputs required, outputs produced, and repetition structure for each step. This process helps you understand the flow of data through your system and identify dependencies between steps, creating a clear data pipeline. It also helps with discovering any missing processes. For each step, specify: Inputs: What data or information does this step need to perform its function? Outputs: What results or transformed data does this step produce? Repetition: Does this step need to be repeated for multiple items or documents? When documenting this information, use a structured format. For example: step1Output = step1Name(input1, input2) step2Output = step2Name(step1Output, input3) Note that inputs for later steps often include outputs from previous steps, illustrating the data flow through your system. Handling Repetition: Be mindful of potential limitations in processing large amounts of data, especially when working with language models. You may need to break down large content into portions and then process each portion through the same set of subsequent steps. To account for this, indicate which steps need to be repeated and for what entities. Use indentation–shifting text to right–to show steps that need to be repeated, and don't use indentation for steps that do not need to be repeated, Let's apply this step to our ongoing example of creating an AI system that generates a PowerPoint presentation from a Word document. We'll break down each step, identifying its inputs, outputs, any repetition, and importantly, the intuition behind each step: content = extractContentFromWordDoc(wordDocFilePath) topics = extractTopics(content) For each topic in topics: contentSegment = extractContentSegment(topic, content) slides = generateSlideContent(topic, contentSegment) ppt = generatePPT(slides) 1. `extractContentFromWordDoc`: - Input: `wordDocFilePath` (the file path of the Word document) - Output: `content` (the extracted text content from the Word document) - Repetition: Performed once for the entire document - Intuition: We start by extracting the raw text from the Word document. This step is crucial because it converts the potentially complex Word format into plain text that our AI system can more easily process. It's the foundation for all subsequent steps. 2. `extractTopics`: - Input: `content` (the extracted text from the previous step) - Output: `topics` (a list or structure of main topics identified in the content) - Repetition: Performed once for the entire content - Intuition: By identifying the main topics, we create a high-level structure for our presentation. This step mimics how a human might skim a document to understand its main points before creating slides. It helps ensure our final presentation will be well-organized and cover all key areas. 3. `For each topic in topics:`: - Intuition: This loop allows us to process each topic individually, which is crucial for managing complexity. Instead of trying to create an entire presentation at once (which could overwhelm our AI), we break it down into more manageable topic-sized chunks. This approach aligns with how humans typically create presentations, focusing on one section at a time. 4. `extractContentSegment`: - Inputs: `topic` (a single topic from the list of topics), `content` (the full text content) - Output: `contentSegment` (the portion of content relevant to the current topic) - Repetition: Repeated for each topic - Intuition: This step is about focusing on relevant information. For each topic, we extract only the content that's pertinent. This helps manage the amount of text our AI needs to process at once, reducing the risk of information overload and improving the relevance of generated slides. 5. `generateSlideContent`: - Inputs: `topic` (the current topic), `contentSegment` (the relevant content for this topic) - Output: `slides` (the content for slides related to this topic) - Repetition: Repeated for each topic - Intuition: Here's where the AI creates the actual slide content, ie slides' titles and their bullet points. By working with one topic and its relevant content at a time, we allow the AI to focus deeply on each section of the presentation. This approach helps ensure that each set of slides is coherent and properly represents its topic. 6. `generatePPT`: - Input: `slides` (all the slide content generated from the previous steps) - Output: `ppt` (the final PowerPoint presentation) - Repetition: Performed once, after all slides have been generated - Intuition: This final step compiles all our generated content into a cohesive PowerPoint presentation. This structure effectively breaks down the complex task of creating a presentation into more manageable steps. The process mimics how a human might approach the task: first understanding the overall content, then identifying main topics, focusing on one topic at a time to create relevant slides, and finally compiling everything into a complete presentation. By using a loop to process topics individually, we address the potential limitation of handling large amounts of data. This approach helps manage the workload for our AI system, potentially improving the accuracy and relevance of the generated slides. Step 4: Assign Tool Type for Each Step After identifying the main steps, their inputs, outputs, and repetition structure, the next crucial task is to assign appropriate tool types to each step. This process helps bridge the gap between high-level planning and implementation, allowing you to think about which steps can be accomplished through coding versus those that require AI models or other specialized tools. For our ongoing example of creating an AI system that generates a PowerPoint presentation from a Word document, let's assign tool types to each step: content = py_extractContentFromWordDoc(wordDocFilePath) topics = llm_extractTopics(content) For each topic in topics: contentSegment = py_extractContentSegment(topic, content) slides = llm_generateSlideContent(topic, contentSegment) ppt = py_generatePPT(slides) Let's break down each step with its assigned tool type and the rationale behind the choice: 1. `py_extractContentFromWordDoc`: - Tool Type: Python function (py_) - Rationale: Extracting text from a Word document is a well-defined task that can be efficiently handled by existing Python libraries like python-docx. This doesn't require the complexity of an AI model and is better suited for a straightforward Python script. 2. `llm_extractTopics`: - Tool Type: Language Model function (llm_) - Rationale: Identifying main topics from a body of text requires understanding context and content, which is well-suited for a language model. This task benefits from the natural language processing capabilities of an LLM. 3. `py_extractContentSegment`: - Tool Type: Python function (py_) - Rationale: Once we have the topics and the full content, extracting relevant segments might seem like a straightforward text processing task. However, a significant challenge arises: topics can appear multiple times throughout the content, and a simple text-matching script wouldn't be able to accurately determine where each topic segment begins and ends. To address this, we can enhance our approach by requesting additional information from the LLM in the previous step. Specifically, we can ask the LLM to provide not just the topics, but also markers (such as the starting lines) for each topic. This additional context allows us to precisely identify where each topic segment begins, greatly simplifying the extraction process. To implement this improved approach, we need to modify our workflow slightly. Here's how the revised flow would look: content = py_extractContentFromWordDoc(wordDocFilePath) topicsAndMarkers = llm_extractTopicsAndMarkers(content) For each {topic, marker} in topicsAndMarkers: markerEnd = get marker for the next topic in topicsAndMarkers contentSegment = py_extractContentSegment(marker, markerEnd, content) slides = llm_generateSlideContent(topic, contentSegment) ppt = py_generatePPT(slides) {topic, marker} in topicsAndMarkers: topicsAndMarkers can be a list or array of tuples, where each tuple contains two elements: a topic and its corresponding marker (starting line). The curly braces {} in the for loop syntax suggest that we're using tuple unpacking to iterate over these pairs of information. markerEnd is determined by getting the marker for the next topic in the topicsAndMarkers list. For each topic except the last one, markerEnd will be the marker of the next topic in the list. For the last one, markerEnd can be none. 4. `llm_generateSlideContent`: - Tool Type: Language Model function (llm_) - Rationale: Creating concise, relevant slide content from a segment of text requires understanding and summarizing information, which is a strength of language models. This step benefits from the natural language generation capabilities of an LLM. 5. `py_generatePPT`: - Tool Type: Python function (py_) - Rationale: Creating a PowerPoint file from structured slide content is a well-defined task that can be handled efficiently by Python libraries like python-pptx. This is more about file manipulation than natural language processing, making it suitable for a Python script. By assigning these tool types, we've created a more detailed roadmap for implementation. This approach allows us to leverage the strengths of different tools: using Python for well-defined, programmatic tasks and language models for tasks requiring natural language understanding and generation. This step in the planning process helps identify which parts of the system will require AI model integration and which parts can be handled by more traditional programming approaches. It provides a clearer picture of the technical requirements for each step and helps in resource allocation and task delegation during the implementation phase. Remember, as emphasized in the document, this planning process is iterative. As you delve deeper into implementation, you may find that some tool type assignments need to be adjusted. The key is to maintain flexibility while progressively refining your plan based on new insights and challenges encountered during development. Step 5: Iterative Refinement Repeat steps 2-4 iteratively, refining your plan each time. Planning an AI system is rarely a linear process. This step encourages you to review and refine your plan multiple times, each iteration bringing more clarity and detail to your system design. During each iteration: Review the logical flow of your system. Does each step naturally lead to the next? Check for consistency between steps, especially in terms of inputs and outputs Ensure that all aspects of your initial goal are addressed Look for opportunities to simplify your design or if you can avoid using an AI model Refine your choice of tools and function names based on a deeper understanding of the requirements Don't be afraid to make significant changes if you identify better approaches. The goal is to have a comprehensive, well-thought-out plan before you start implementation. Step 6: Create prompt templates Once, your design has finalized, write down prompt templates for LLM type steps. While writing prompt templates you would likely use many, if not all, of the inputs you identified for the step. You would also want to pay close attention to the step's objective and desired output when formulating your prompt. These elements should guide the structure and content of your prompt template. A critical consideration in prompt engineering is managing complexity. While modern LLMs have impressive reasoning capabilities, their performance can degrade when faced with overly complex prompts requiring multiple operations. Hence, it's essential to take an iterative approach to prompt design. Test your prompts on an LLM interface to gauge their effectiveness. This hands-on testing allows you to quickly identify whether you need to break a step further into smaller, simpler sub-steps. Prompt for extractTopicsAndMarkers Since a topic can repeat in document and it may not be a good marker for dividing the document into portions. Hence, we would use first two starting lines of the key topic as marker. The starting lines of the next key topic will signify the end of the section for a given key topic. Analyze the given document and extract key topics, following these guidelines: 1. Key Topic Identification: - Topics should represent major sections or themes in the document. - Each key topic should be substantial enough for at least one slide with 3-5 bullet points, potentially spanning multiple slides. - Topics should be broad enough to encompass multiple related points but specific enough to avoid overlap. - Identify topics in the order they appear in the document. - Consider a new topic when there's a clear shift in the main subject, signaled by transitional phrases, new headings, or a distinct change in content focus. - If a topic recurs, don't create a new entry unless it's substantially expanded upon. 2. Key Topic Documentation: - For each key topic, create a detailed name that sums up the idea of the section or theme it represents. - Next, provide the first ten words of the section that the key topic represents. 3. Provide the output in the following format: **key topic 1** first ten words of the section or theme that the key topic 1 represents **key topic 2** first ten words of the section or theme that the key topic 2 represents Document to analyze: ''' {{content}} ''' Prompt for generateSlideContent You will be given a key topic, and a document portion, which provide detail about the key topic. Your task is to create slides based on the document portion. Follow these steps: 1. Identify the relevant section of the document between the given starting lines. 2. Analyze this section and create slides with titles and bullet points. Guidelines: - The number of slides can be as few as one and as many as 10, depending on the amount of non-repetitive information in the relevant section of the key topic. - Present slides in the order that the information appears in the document. - Each slide should have 4-6 concise bullet points, each containing a single key idea or fact. - Use concise phrases or short sentences for bullet points, focusing on conveying key information clearly and succinctly. - If information seems relevant to multiple topics, include it in the current topic's slides, as it appears first in the document. - Avoid redundancy across slides within the same key topic. Output Format: **paste slide title here** paste point 1 here paste point 2 here paste point 3 here Inputs: Key Topic: '''{{topic}}''' Document portion:''' {{contentSegment}} ''' Please create slides based on the document portion, following the guidelines provided. Ensure that the slides comprehensively cover the key topic without unnecessary repetition. We will need to process the response generated by llm_extractTopicsAndMarkers, and convert it into JSON format for subsequent steps. Further, let us also save the llm outputs locally. It would help us evaluate the outputs. content = py_extractContentFromWordDoc(wordDocFilePath) extractTopicsMarkersPrompt = py_generatePrompt(extractTopicsMarkersPromptTemplate, vars={content}) topicsAndMarkers = llm_extractTopicsAndMarkers(extractTopicsMarkersPrompt) py_saveFile(topicsAndMarkersFilePath, topicsAndMarkers) topicsMarkersJson = py_convertTextToJson(topicsAndMarkers) For each i, {topic, marker} in topicsMarkersJson: startPostion = py_getMarkerPostion(marker, content) {marker} = topicsMarkersJson[i+1] endPostion = py_getMarkerPostion(marker, content) contentSegment = py_extractContentSegment(startPostion, endPostion, content) topicWithContentSegment = topicWithContentSegment +"\n\n**" + topic + "**\n" + contentSegment generateSlideContentPrompt = py_generatePrompt(generateSlideContentPromptTemplate, vars={topic, contentSegment}) slides = slides + llm_generateSlideContent(generateSlideContentPrompt) py_saveFile(topicWithContentFilePath, topicWithContent) py_saveFile(slideContentFilePath, slides) ppt = py_generatePPT(slides) Step 7: Detailed Implementation Requirements (optional) For each step, describe the key operations that need to be performed. This step helps you dive deeper into each broad step, bridging the gap between high-level steps and specific implementation. By outlining the operations within each step, you could potentially use LLMs to write the complete code. Depending on how well you describe the operations, your code accuracy could be more than ninety percent. Having said that, debugging and integrating code in the broader multi-tier architecture requires coding experience. For implementation of Powerpoint creation: Please download the zipped file and extract them Make sure you have installed VSCode and Python in it. (Refer to this tutorial if needed: https://chatgpt.com/share/dfa8bbc9-dc52-4a2b-bd31-90154fad2b3d) Create .env file in the folder ANTHROPIC_API_KEY="" Add your API key: https://console.anthropic.com/dashboard Paste a doc.docx in the folder Exercise: AI system for Analyzing Reviews Let us understand the implementation with the following example: Step 1: Define the Goal, Input, and Output Goal: Create an AI system that analyzes customer reviews for products to extract sentiment and key features, providing insights for product improvement and customer satisfaction. Input: A list of products and their associated customer reviews. Output: A summary report for each product containing sentiment analysis, key features mentioned, and overall insights. Step 2: Identify Broad Step Names collectReviews extractInfo analyzeUsingML generateReport Step 3: Identify Inputs, Outputs, and Repetition for Each Step For each product in products: reviews = collectReviews(product) For each review in reviews: featuresAndSentiments = extractInfo(review) mlModelResults = analyzeUsingML(featuresAndSentiments for all reviews) finalReport = generateReport(mlModelResults for all products) Step 4: Assign Tool Type for Each Step For each product in products: reviews = api_collectReviews(product) For each review in reviews: featuresAndSentiments = llm_extractInfo(review) mlModelResults = py_analyzeUsingML(featuresAndSentiments for all products and reviews) finalReport = llm_generateReport(mlModelResults) Check Model's Understanding of AI implementation plan The following prompt will help you evaluate the model's understanding of your AI system implementation plan. Insert the outputs from Step 1 and Step 4 in the designated areas within the prompt. Analyze the provided goal, inputs, output, and pseudo-code for an AI system. Generate explanations by following the steps given below: 1) function list: Get all the functions mentioned in the pseudo-code. Function names in the pseudo-code have prefixes such as py, llm, and ml. Following is the definition of prefixes: py<function> : suggests that this function is a python method llm<function> : suggests that this function makes an api call to a LLM model ml<function>: suggets that this function calls a machine learning model 2) pseudo-code explanation: Explain the pseudo-code and its flow. 3) function explanations: Generate explanation for each function in the pseudo-code covering detail: a. Expected input parameters b. Expected output c. list of operations that need to be done in the function. Output Format: Provide the explanations in the following structure: *pseudo-code explanation <pseudo-code explanation> **<function 1> <function 1 explanation> **<function 2> <function 2 explanation> Goal: <paste goal here> Inputs: <paste inputs here> Output: <paste output here> pseudo code: """ <paste pseudo code> """

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Prompting for Multi-Step Processes: Stepwise vs. Prompt Chaining

TL;DR: When working with complex, multi-step tasks in language models, 🔗 Prompt Chaining offers precise control by breaking the process into discrete steps, while Stepwise Prompting combines all steps into a single prompt for efficiency. Start with stepwise prompting for simpler tasks, and switch to prompt chaining if quality or consistency declines. Prompting for multi-step processes Prompting often involves instructing models to produce responses for complex, multi-step processes. Try to generate prompt for the following scenario. This scenario involves processing a document through three main steps: Summarizing each paragraph of the document in one line. Extracting key points from the entire document. Organizing the generated summary lines under their respective key points in the order they appear in the original document. This task requires a combination of summarization, key point extraction, and organizational skills. It's a complex process that involves understanding the document's structure, content, and main themes. The two popular ways to handle such tasks are: Prompt Chaining, and Stepwise Prompting. Prompt chaining Prompt chaining is an approach for refining outputs from large language models (LLMs) that involves using a series of discrete prompts to guide the model through different phases of a task. This method allows for a more structured and controlled approach to refinement, with each step having a specific focus. By breaking down complex tasks into smaller, more manageable prompts, prompt chaining provides greater flexibility and precision in directing the LLM's output. For example, in a text summarization task, one prompt might focus on extracting key points, another on organizing them coherently, and a final prompt on polishing the language. Stepwise prompting Stepwise prompting, on the other hand, integrates all phases of the task within a single prompt. This approach attempts to guide the LLM through the entire process in one go, challenging the model to generate a longer and more complex output based on a single set of instructions. While simpler to implement, stepwise prompting may be less effective for complex tasks that benefit from a more granular approach. For instance, when summarizing a lengthy academic paper, a single stepwise prompt might struggle to capture all the nuances of content selection, organization, and style that separate prompts in a chain could address individually. Further, you can improve the clarity by assigning step names followed by step explanation. For example: 1) Analysis: Analyze the given text for key themes. 2) Summary: Summarize the themes identified in `Analysis`. 3) Conclusion: Draw conclusions based on the `Summary`. The use of backticks to reference previous outputs leaves no ambiguity. When to Use Step Names in Stepwise Prompts? Use step names when the output of a step will be referenced later in the prompt or in subsequent prompts. Step names help in organization and add to clarity, especially for tasks with multiple interdependent steps. Additionally, it can help the model more effectively associate output variables with the specific steps in which they are extracted. When Step Names Might Be Optional? 1. Simple, Linear Tasks: For straightforward tasks with clear progression, step names might be unnecessary. 2. Short Prompts: In brief prompts with only 2-3 steps, numbering alone might suffice. Best Practices - Be consistent: If you use step names, use them for all steps in the prompt. - Keep names short and descriptive: Use clear, concise labels that indicate the purpose of each step. Illustration of Stepwise prompting and Prompt Chaining: Generate document outline Let's create prompts for the scenario explained earlier using both techniques: Prompt Chaining: For prompt chaining, we'll break down the task into three separate prompts, each focusing on a specific subtask. Prompt 1 (Paragraph Summarization): You are tasked with summarizing a document. For each paragraph in the given document, create a one-line summary that captures its main idea. Please provide these summary lines in a numbered list, with each number corresponding to the paragraph number in the original document. Prompt 2 (Key Point Extraction): Based on the entire document, identify and list the main key points. These should be the overarching themes or crucial ideas that span multiple paragraphs. Present these key points in a bulleted list. Prompt 3 (Organization): You will be provided with two lists: one containing one-line summaries of each paragraph, and another containing key points extracted from the document. Go through the `Document`. Your task is to organize the summary lines from `Summary list` under their most relevant key points from `Key point list`. Maintain the original order of the summary lines within each key point. Present the result as a structured list with key points as main headings and relevant summary lines as sub-points. Output can be in the following format: **<key point 1> <summary line 1> <summary line 2> **<key point 2> <summary line 3> <summary line 4> <summary line 5> Ensure that all summary lines are included and that they maintain their original numbering order within each key point category. Document: It is the document that needs to be converted into topic-wise `summary list` Summary list: It is a list of one-line summaries of each paragraph in the `document` Key point list: It is a list of key points covered in the `Document` Stepwise prompting: The prompt below demonstrates how we can replicate the three distinct steps previously used in prompt chaining within a single step by using stepwise prompting. You are tasked with analyzing, summarizing, and organizing the content of a given document. Please follow these steps in order: 1. Read document: Carefully read through the entire document. 2. Summary list: Create a one-line summary for each paragraph, capturing its main idea. Number these summaries according to the paragraph they represent. 3. Key point list: Identify the main key points of the entire document. These should be overarching themes or crucial ideas that span multiple paragraphs. 4. Key point wise Summary list: Organize the `Summary list` under their most relevant key points from `Key point list`. Maintain the original order of the summary lines within each key point. Present the result as a structured list with key points as main headings and relevant summary lines as sub-points. The output should be in the following format: **<key point 1> <summary line 1> <summary line 2> **<key point 2> <summary line 3> <summary line 4> <summary line 5> Ensure that all summary lines are included and that they maintain their original numbering order within each key point category. Missing paragraphs or lines Notice the last line: Ensure that all summary lines are included and that they maintain their original numbering order within each key point category. Despite our repeated efforts to ensure the AI model analyzes every paragraph and line, it continues to overlook some. Models may consider multiple paragraphs as one paragraph, or may skip paragraphs completely. This is much common problem when processing long documents. Similarly, if you have to process each line in a paragraph, model may combine lines or skip lines. The solution is you should consider breaking the document into smaller chunks and process each chunk separately. Further, I've seen better results if you provide numbered list of paragraphs, and ask it to generate output as the numbered list corresponding to each paragraph. This makes it easier for the AI model to keep track of paragraphs. Same goes for line processing within paragraphs. Stepwise prompting vs Prompt Chaining Here's a comparison of prompt chaining and stepwise prompting in table format: Aspects Prompt Chaining Stepwise Prompting Execution Runs the LLM multiple times, with each step focusing on a specific subtask Completes all phases within a single generation, requiring only one run of the LLM Complexity and Control Allows for more precise control over each phase of the task, but requires more comprehensive prompts from humans Uses a simpler prompt containing sequential steps, but challenges the LLM to generate a longer and more complex output Effectiveness Generally yields better results, especially in text summarization tasks Might produce a simulated refinement process rather than a genuine one, potentially limiting its effectiveness Task Breakdown Excels at breaking down complex tasks into smaller, more manageable prompts Attempts to handle the entire task in a single, more complex prompt Iterative Improvement Allows for easier iteration and improvement of individual steps in the process Less flexible for targeted improvements without modifying the entire prompt Resource Usage May require more computational resources due to multiple LLM runs More efficient in terms of API calls or processing time Learning Curve Higher initial complexity for prompt designers, but potentially more intuitive for complex tasks Simpler to implement initially, but may be challenging to optimize for complex tasks Recommendation for choosing between the two: I recommend starting with stepwise prompting, as it is a more cost-effective solution and requires less engineering effort compared to prompt chaining. However, if you notice a decline in quality or inconsistent results, switching to prompt chaining will be necessary. Which approach do you prefer when working with LLMs? Let’s discuss!

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Multi-LoRA inference: Serve thousands of fine-tuned LLMs on a single GPU

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LoRA Demystified: Optimizing Language Models with Low-Rank Adaptation

1. Introduction to LoRA Hey there, language model enthusiasts! Today, we're diving into the fascinating world of LoRA - Low-Rank Adaptation. If you've been keeping up with the latest trends in fine-tuning large language models, you've probably heard this term buzzing around. But what exactly is LoRA, and why should you care? Let's break it down!Have you ever wished you could fine-tune a massive language model without breaking the bank or waiting for days? Enter LoRA - the game-changing technique that's revolutionizing how we adapt large language models. If you've been keeping up with the AI world, you've likely heard whispers about LoRA, but maybe you're not quite sure what all the fuss is about. Well, buckle up, because we're about to embark on a journey that will demystify LoRA and show you how it's reshaping the landscape of language model optimization. Imagine being able to tailor a behemoth language model to your specific needs without the hefty computational costs typically associated with fine-tuning. That's the magic of LoRA, or Low-Rank Adaptation. In a world where AI models are growing exponentially in size and complexity, LoRA emerges as a beacon of efficiency, allowing us to adapt these digital giants with surgical precision. In this article, we're going to pull back the curtain on LoRA. We'll start by unraveling what LoRA is and why it's causing such a stir in the AI community. Then, we'll roll up our sleeves and dive into the nitty-gritty of how LoRA works, from its clever use of low-rank matrix decomposition to its seamless integration with pre-trained models. But we won't stop at theory. We'll guide you through implementing LoRA in PyTorch, breaking down the process into manageable chunks. You'll learn how to create LoRA layers, wrap them around your favorite pre-trained model, and orchestrate a forward pass that leverages the power of LoRA. We'll also explore best practices for using LoRA, from choosing the right rank parameter to optimizing the scaling factor. And for those ready to push the boundaries, we'll delve into advanced techniques that can take your LoRA implementations to the next level. Whether you're an AI researcher looking to streamline your model adaptation process, a developer aiming to make the most of limited computational resources, or simply an enthusiast curious about the cutting edge of language model optimization, this article has something for you. So, are you ready to unlock the potential of LoRA and revolutionize how you work with large language models? Let's dive in and demystify LoRA together! What is LoRA? LoRA, short for Low-Rank Adaptation, is a clever technique that's revolutionizing how we fine-tune large language models. Introduced by Hu et al. in 2022, LoRA allows us to adapt pre-trained models to specific tasks without the hefty computational cost typically associated with full fine-tuning. At its core, LoRA works by adding small, trainable matrices to each layer of the Transformer architecture. These matrices are decomposed into low-rank representations, hence the name. The beauty of this approach is that it keeps the original pre-trained model weights untouched while introducing a minimal number of new parameters to learn. Benefits of LoRA for Language Model Fine-Tuning Now, you might be wondering, "Why should I use LoRA instead of traditional fine-tuning?" Great question! Here are some compelling reasons: Efficiency: LoRA dramatically reduces the number of trainable parameters, making fine-tuning faster and less resource-intensive. Cost-effectiveness: With fewer parameters to train, you can save on computational costs and energy consumption. Flexibility: LoRA allows you to create multiple task-specific adaptations of a single base model without the need for full fine-tuning each time. Performance: Despite its simplicity, LoRA often achieves comparable or even better performance than full fine-tuning for many tasks. 2. Understanding the LoRA Architecture Before we dive into the implementation, let's take a moment to understand how LoRA works under the hood. This knowledge will help you appreciate the elegance of the technique and make informed decisions when using it. Low-Rank Matrix Decomposition The key idea behind LoRA is low-rank matrix decomposition. In linear algebra, a low-rank matrix is one that can be approximated by the product of two smaller matrices. LoRA leverages this concept to create efficient adaptations. Instead of learning a full matrix of weights for each layer, LoRA introduces two smaller matrices, A and B. The adaptation is then computed as the product of these matrices, scaled by a small factor. Mathematically, it looks like this: LoRA adaptation = α * (A * B) Where: A is a matrix of size (input_dim, r) B is a matrix of size (r, output_dim) r is the rank, typically much smaller than input_dim and output_dim α is a scaling factor This decomposition allows us to capture the most important directions of change in the weight space using far fewer parameters. Integration with Pre-Trained Models LoRA integrates seamlessly with pre-trained models. Here's how it works: The original weights of the pre-trained model are frozen (not updated during training). LoRA layers are added in parallel to the existing linear layers in the model. During the forward pass, the output of the original layer and the LoRA layer are summed. Only the LoRA layers are updated during training, leaving the base model untouched. This approach allows us to adapt the model's behavior without modifying its original knowledge, resulting in efficient and effective fine-tuning. 3. Implementing LoRA in PyTorch Now that we understand the theory, let's roll up our sleeves and implement LoRA in PyTorch! We'll break this down into three main components: the LoRA Layer, the LoRA Model, and the forward pass. 3.1 LoRA Layer Implementation First, let's create our LoRA Layer. This is where the magic happens! ```python import torch import torch.nn as nn class LoRALayer(nn.Module): def __init__(self, in_features, out_features, rank=4): super().__init__() self.lora_A = nn.Parameter(torch.randn(in_features, rank)) self.lora_B = nn.Parameter(torch.zeros(rank, out_features)) self.scaling = 0.01 def forward(self, x): return self.scaling * (x @ self.lora_A @ self.lora_B) ``` Let's break this down: We define a new `LoRALayer` class that inherits from `nn.Module`. In the constructor, we create two parameter matrices: `lora_A` and `lora_B`. Notice that `lora_A` is initialized randomly, while `lora_B` starts as all zeros. The `scaling` factor is set to 0.01. This small value helps to keep the LoRA adaptation subtle at the beginning of training. In the forward pass, we compute the LoRA adaptation by multiplying the input `x` with `lora_A` and `lora_B`, then scaling the result. 3.2 LoRA Model Implementation Now that we have our LoRA Layer, let's create a LoRA Model that wraps around our base pre-trained model: ```python class LoRAModel(nn.Module): def __init__(self, base_model): super().__init__() self.base_model = base_model self.lora_layers = nn.ModuleDict() # Add LoRA layers to relevant parts of the base model for name, module in self.base_model.named_modules(): if isinstance(module, nn.Linear): self.lora_layers[name] = LoRALayer(module.in_features, module.out_features) ``` Here's what's happening: We create a `LoRAModel` class that takes a `base_model` as input. We iterate through all modules in the base model, looking for linear layers. For each linear layer, we create a corresponding LoRA layer and add it to our `lora_layers` dictionary. This approach allows us to selectively apply LoRA to specific layers of the model, typically focusing on the attention and feed-forward layers in a Transformer architecture. 3.3 LoRA Model Forward Pass Finally, let's implement the forward pass for our LoRA Model: ```python def forward(self, x): # Forward pass through base model, adding LoRA outputs where applicable for name, module in self.base_model.named_modules(): if name in self.lora_layers: x = module(x) + self.lora_layers[name](x) else: x = module(x) return x ``` In this forward pass: We iterate through the modules of the base model. If a module has a corresponding LoRA layer, we add the LoRA output to the base module's output. For modules without LoRA, we simply pass the input through as usual. This implementation ensures that the LoRA adaptations are applied exactly where we want them, while leaving the rest of the model unchanged. 4. Using the LoRA Model Great job! Now that we have our LoRA model implemented, let's talk about how to use it effectively. Training Process Training a LoRA model is similar to training any other PyTorch model, with a few key differences: Freeze the base model parameters: ```python for param in model.base_model.parameters(): param.requires_grad = False ``` Only optimize the LoRA parameters: ```python optimizer = torch.optim.AdamW(model.lora_layers.parameters(), lr=1e-3) ``` Train as usual, but remember that you're only updating the LoRA layers: ```python for epoch in range(num_epochs): for batch in dataloader: optimizer.zero_grad() output = model(batch) loss = criterion(output, targets) loss.backward() optimizer.step() ``` Inference with LoRA-Adapted Models When it's time to use your LoRA-adapted model for inference, you can simply use it like any other PyTorch model: ```python model.eval() with torch.no_grad(): output = model(input_data) ``` The beauty of LoRA is that you can easily switch between different adaptations by changing the LoRA layers, all while keeping the same base model. 5. Best Practices As you start experimenting with LoRA, keep these best practices in mind: Choosing the Rank Parameter The rank parameter (r) in LoRA determines the complexity of the adaptation. A higher rank allows for more expressive adaptations but increases the number of parameters. Start with a small rank (e.g., 4 or 8) and increase if needed. Scaling Factor Optimization The scaling factor (α) in the LoRA layer can significantly impact performance. While we set it to 0.01 in our example, you might want to treat it as a hyperparameter and tune it for your specific task. Performance Comparisons Always compare your LoRA-adapted model's performance with a fully fine-tuned model. In many cases, LoRA can achieve comparable or better results with far fewer parameters, but it's essential to verify this for your specific use case. 6. Advanced LoRA Techniques Ready to take your LoRA skills to the next level? Here are some advanced techniques to explore: Hyperparameter Tuning for the Scaling Factor Instead of using a fixed scaling factor, you can make it learnable: ```python self.scaling = nn.Parameter(torch.ones(1)) ``` This allows the model to adjust the impact of the LoRA adaptation during training. Selective Application of LoRA You might not need to apply LoRA to every layer. Experiment with applying it only to specific layers (e.g., only to attention layers) to find the best trade-off between adaptation and efficiency. Freezing Base Model Parameters We touched on this earlier, but it's crucial to ensure your base model parameters are frozen: ```python for param in model.base_model.parameters(): param.requires_grad = False ``` This ensures that only the LoRA parameters are updated during training. And there you have it! You're now equipped with the knowledge to implement and use LoRA for optimizing language models. Remember, the key to mastering LoRA is experimentation. Don't be afraid to try different configurations and see what works best for your specific use case. Happy adapting, and may your language models be ever more efficient and effective! Summary In this article, we've demystified LoRA (Low-Rank Adaptation), a powerful technique for optimizing large language models. We explored how LoRA enables efficient fine-tuning by introducing small, trainable matrices to pre-trained models, dramatically reducing computational costs while maintaining performance. We delved into the LoRA architecture, explaining its use of low-rank matrix decomposition and seamless integration with pre-trained models. We then provided a step-by-step guide to implementing LoRA in PyTorch, covering the creation of LoRA layers, wrapping them around base models, and executing forward passes. Key takeaways include: LoRA offers a cost-effective and flexible approach to adapting large language models. Implementing LoRA involves creating specialized layers and integrating them with existing model architectures. Best practices such as choosing appropriate rank parameters and optimizing scaling factors are crucial for success. Advanced techniques like learnable scaling factors and selective application can further enhance LoRA's effectiveness. As AI models continue to grow in size and complexity, techniques like LoRA become increasingly valuable. Whether you're an AI researcher, developer, or enthusiast, LoRA opens up new possibilities for working with large language models.

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My Journey to Building an AI Agent: Extracting Insights from Social Media

Introduction Artificial Intelligence (AI) is transforming industries across the globe, and its influence on social media management is particularly noteworthy. AI agents can analyze vast amounts of data, uncovering patterns and trends that are otherwise difficult to detect. As part of my Master of Science degree program in Information Systems at the University of Utah, I faced a pivotal decision: complete a certification course or take on a capstone project. Drawn to the challenge and potential for growth, I chose the latter. Under the guidance of Prof. Rohit Aggarwal from the Information Systems Department at the David Eccles School of Business, I embarked on an exciting journey to build an AI agent capable of revolutionizing social media posts. Little did I know that this project would push me far beyond the boundaries of my classroom knowledge and into the realm of practical, cutting-edge AI application. Seizing the Opportunity: The Beginning of My AI Journey The project's scope was ambitious: develop an AI system that could analyze historical social media content and generate future posts to drive engagement. I chose Instagram as the primary platform, focusing on strategies employed by industry leaders like Ogilvy, BBDO, AKQA, and MCCANN. These companies, known for their expertise in brand promotion, provided valuable insights into audience engagement that I could leverage in my AI agent. My task involved collecting data from company websites and social media platforms through web scraping, processing it, and utilizing AI models to extract meaningful themes. The ultimate goal was to generate future posts that would drive engagement and align with each company's brand. This project would test not only my technical skills but also my ability to understand and apply marketing strategies in a digital context. The Journey: Building the AI Agent Data Collection and Preprocessing My first major hurdle was data collection through web scraping. While I had some exposure to Python in my classes, this project demanded a level of expertise I hadn't yet achieved. I spent countless hours poring over YouTube tutorials, documentation, and online forums to master the intricacies of web scraping. I learned to use Python libraries like Instaloader to obtain data from Instagram pages, and Selenium and BeautifulSoup to scrape company websites. This process yielded valuable information, including captions, shares, likes, and comments from each company's Instagram account. After collecting the data, I moved on to preprocessing. I cleaned the data by removing duplicates and null values, converted dates to a datetime format, and prepared it for analysis. Ensuring that the data was accurate and well-organized was crucial for setting a solid foundation for theme extraction. This step taught me the importance of data quality in AI projects, a concept that was only briefly touched upon in my coursework. Theme Extraction and Grouping With the data ready, I conducted theme extraction using the Gemma2b model from Ollama. This was a significant leap from the basic machine learning concepts I had learned in class. I employed zero-shot prompting, a method where I asked the model to perform tasks it hadn't been explicitly trained on. By providing suggested themes, I guided Gemma2b to extract relevant themes from the Instagram posts, such as 'Product Announcement' and ‘Customer Story.’ Once I extracted the themes, I grouped and normalized them. I used Gemma2b to categorize the themes into more concise groups, ensuring that similar themes like 'Customer Story' and 'Customer Stories' were treated as one. This normalization was essential for scaling the data effectively, teaching me about the nuances of natural language processing and the importance of context in AI-driven text analysis. Engagement Analysis and Generating Future Posts Next, I conducted an engagement analysis by calculating scores for each theme based on likes, shares, and comments. Summing up these metrics helped me identify the top 10 themes across all companies. This analysis revealed which themes were driving engagement and how companies like Ogilvy and AKQA were leveraging these strategies. This step required me to blend my understanding of social media metrics with data analysis techniques, bridging the gap between marketing concepts and technical implementation. Armed with this analysis, I used Gemma2b to generate future social media posts. I crafted these posts based on the successful strategies I identified, with suggestions for images, videos, captions, and hashtags. I also included a predicted engagement score for each post, aiding social media managers in planning their content effectively. This phase of the project was particularly exciting as it allowed me to see the practical application of AI in content creation, a concept far beyond what I had learned in my classes. To make my AI agent accessible, I developed an interactive interface using Streamlit. This user-friendly platform allowed social media managers to interact with the model, generate posts, and visualize engagement predictions. Creating this interface pushed me to learn about web application development and user experience design, areas that were entirely new to me but crucial for making my AI agent practical and usable. Challenges I Faced Throughout this project, I encountered numerous challenges that pushed me far beyond what I had learned in my classes: Web Scraping Implementation: Despite my theoretical knowledge of web scraping in Python, this project demanded practical application at a much higher level. I had to enhance my skills through intensive study of YouTube tutorials and comprehensive reading on the subject, including its legal implications to ensure compliance. Model Selection and Deployment: I initially explored quantized models for local execution, gaining extensive knowledge about their capabilities and limitations. After considering various options, including GPU-dependent models, I settled on Gemma 2b with Ollama due to its compatibility with my local machine's resources. This decision came after attempting to use Google Colab's enhanced GPU environment, which proved financially unfeasible for my project's scope. Development Environment Setup: Setting up the working environment posed its own challenges. I opted for Visual Studio Code, which provided a robust platform for code structuring and debugging the large language model. This choice significantly improved my workflow efficiency, but required me to learn a new development environment. Data Processing and Analysis: Data cleaning and merging CSV files presented initial hurdles. I overcame these by developing Python scripts to streamline these processes. The most significant challenge was extracting themes from the large dataset using Gemma 2b, which required substantial computational time. To address this, I utilized a high-RAM system and implemented checkpoints in my code to manage the process more effectively. Model Fine-tuning and Result Validation: To ensure the extracted themes aligned with the desired format, I implemented a training method using sample themes. This was followed by a meticulous manual review process to verify the accuracy and relevance of the extracted themes. Post-processing and Application Development: Once I extracted themes, I leveraged the model to categorically group them and align them with engagement metrics. Additionally, I used Gemma to generate weekly posts designed to resonate with the target audience. The final step involved developing a Streamlit application to generate prompt responses, providing a user-friendly interface for accessing the project's insights. Lessons Learned and Conclusion Despite the difficulties, this project provided me with invaluable lessons. I honed my coding skills, mastered the intricacies of web scraping, and gained hands-on experience with machine learning models. Additionally, the project emphasized the importance of adaptability, communication, and project management—skills that are crucial for success in any professional setting. Building this AI agent was a transformative experience for me. It not only equipped me with technical skills but also prepared me for future roles in AI and data analytics. My project demonstrated the potential of AI in enhancing social media management and underscored the importance of understanding data to make informed decisions. Looking ahead, I'm excited about the possibilities AI offers and the role I can play in shaping this technology. This experience has not only provided me with technical skills but also ignited a passion for creating AI solutions that can make a real difference in how businesses understand and interact with their digital audience. My journey of building my first AI agent has laid a solid foundation for future projects, and I have a strong desire to continue learning and growing in this dynamic field.

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Opinion: Teaching Ai & Critical Thinking

Opinion: Teaching Ai & Critical Thinking The opinions expressed herein are derived from our research and own experiences in: developing a few AI Agents, observing student engagement across different variations of our AI classes, engaging in discussions within AI committees and with attendees of BizAI 2024. The Growing Importance of Critical Thinking in the AI Era In the new era of artificial intelligence (AI) and large language models (LLMs), critical thinking skills have become more important than ever before. A 2022 survey by the World Economic Forum revealed that 78% of executives believed critical thinking would be a top-three skill for employees in the next five years, up from 56% in 2020 [1]. As AI systems become more advanced and capable of performing a wide range of tasks, it is crucial for humans to develop and maintain strong critical thinking abilities to effectively leverage these tools and make informed decisions. The Necessity of Human Insight for Effective AI Utilization One of the key reasons critical thinking is so valuable in this context is that LLMs excel at providing information and executing tasks based on their training data, but they often struggle with higher-level reasoning, problem decomposition, and decision-making. While an LLM can generate code, write articles, or answer questions, it may not always understand the broader context or implications of the task at hand. This is where human critical thinking comes into play. For example, let's consider a scenario where a company wants to develop a new product. An LLM can assist by generating ideas, conducting market research, and even creating a project plan. However, it is up to the human decision-makers to critically evaluate the generated ideas, assess their feasibility and potential impact, and direct the AI model where it made mistake or missed something significant such as company values, long-term goals, and potential risks. Cultivating Critical Thinking Skills for AI Collaboration Moreover, as the value of knowing "how" to perform a task decreases due to the capabilities of LLMs, the value of knowing "what" to do and "why" increases. Because AI can manage a lot of "how" to perform a task, it frees professionals to focus on "what" and "why". By developing strong critical thinking abilities, professionals can effectively collaborate with AI systems, leveraging their strengths while compensating for their limitations. This synergy between human reasoning and AI capabilities has the potential to make professionals more productive, bring costs down and help companies grow manifold. However, it is important to note that critical thinking skills must be actively cultivated and practiced. As professors, we need to think of ways to teach students with the tools and training necessary to thrive in an AI-driven world. Let us consider an example of how AI and critical thinking can be taught in tandem. Example: Teaching AI and Critical Thinking in Tandem In one of our courses we teach students how to effectively use AI models to augment their thought process and plan AI agents for revamping business processes. Students explore how to plan an AI agent that learns the tacit knowledge, which experts develop over years of experience. Further, how another AI agent can use this tacit knowledge in conjunction with Retrieval Augmented Generation (RAG) as part of its context to generate decisions or content that mimics the complex decision making of an expert. Through this process, students not only learn technical skills related to AI and LLMs but also develop essential critical thinking abilities such as problem decomposition, strategic planning, and effective communication. They learn to view AI as a tool to augment and enhance their own thinking, rather than a replacement for human judgment and decision-making. They also have better understanding of the limitations of AI models. These AI models solve a lot of "how" type problems that professionals earlier had to spend significant time learning, planning and working on. However, these models also come with their own set of challenges such as context window, limited reasoning abilities, and variability in responses. Hence, there is strong need for students to prepare for AI integration in workplaces accounting for AI models' limitations. Educating students to remain in control Teaching students to view LLMs as highly knowledgeable assistants that sometimes get confused and need direction is a valuable approach. It encourages students to take an active role in guiding and correcting the AI, rather than simply accepting its outputs at face value. They recognize that while AI can provide valuable insights and generate ideas, it is ultimately up to humans to critically evaluate and act upon that information. This understanding helps students develop a healthy and productive relationship with AI, one in which they are in control and can effectively leverage these tools to support their own learning and growth. Intellectual laziness & associated risks While the collaboration between humans and AI presents numerous opportunities, it is essential to be aware of potential drawbacks and risks. As AI models become more advanced and capable, there is a genuine concern that some early learners, may become overly dependent on these tools. This over-reliance could diminish their critical thinking and problem-solving abilities, possibly fostering "intellectual laziness." Individuals might become less inclined to learn and explore new concepts on their own, relying instead on AI for answers. Further, they may lose faith in their own judgment and may stop questioning the AI model's output. In one of our research studies, we observe this behavior among early software developers who start relying on AI models too much. This situation could widen the divide between those who use AI to boost their productivity and those who lean on it too much. To counter these risks, it's important that, along with fostering critical-thinking abilities, we need to stress the need for critical engagement with AI. We should encourage students to scrutinize and question the outputs of AI actively. They need to help students see that excessive reliance on AI can lead to a lack of depth in understanding and personal growth. By advocating for a strategy that equally values AI resources and independent thinking skills, we can guide learners through this new landscape successfully. As we look towards the future, the increasing importance of critical thinking skills in the AI era will have significant implications for job markets and educational curricula. Professionals who can effectively collaborate with AI systems and leverage their capabilities will be in high demand. Hence, faculty will need to adapt their programs to ensure that students understand the importance of using AI as a tool to augment their thinking and not as a replacement. Further, we must rethink our courses and integrate more emphasis on the "what", challenging students to apply their critical thinking skills to real-world problems and decision-making scenarios. Invite our colleagues for collaboration This is not a trivial task, and it will require collaboration and idea-sharing among faculty members. We have been actively exploring these issues and would greatly value the perspectives and insights of our colleagues on this topic. We welcome further discussions and encourage you to reach out to us to share your thoughts and experiences. Disclaimer It's important to note that these insights are primarily anecdotal and have not undergone scientific scrutiny. Additionally, the research involving developers where we noted instances of intellectual laziness has not been validated yet through peer review. References World Economic Forum. (2022). The Future of Jobs Report 2022. Geneva, Switzerland.

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