Exercise 4 & 5: RAG with ReAct (+ Reflection) for 10-K filings

The objective of this exercise series is to develop a prototype of a Retrieval-Augmented Generation (RAG) system capable of answering questions based on 10-K filings submitted to the U.S. Securities and Exchange Commission (SEC). The full series includes six Colab notebooks, each exploring progressively advanced concepts in RAG systems and their applications:

• Exercise 1: Simple RAG for 10-K filings
   
• Exercise 2: RAG with Reranker for 10-K filings
   
• Exercise 3: RAG with Query Decomposition & Tracing with LangSmith/LangFuse
   
• Exercise 4: RAG with Agentic Pattern: ReAct (Reasoning + Action) 
  Code with Explanation is posted here: Colab Notebook Link
   
• Exercise 5: RAG with Agentic Pattern: ReAct + Reflection
  Code with Explanation is posted here: Colab Notebook Link
  
   

These exercises incrementally build on basic RAG with focus on “why” before “what” and “how".

In the previous exercise, we explored how to break down a complex query into sub-queries, retrieve relevant chunks from a vector database for each sub-query, and generate answers based on those chunks. However, there are instances where the necessary knowledge to answer a user's question may not be available in our vector databases.

In such cases, we need to equip our system with pre-built tools that can fetch information from external sources. Specifically, in the Colab notebook, we demonstrate how to retrieve LinkedIn handles of directors listed in SEC filings. To achieve this, we utilize a set of tools, as illustrated in the following diagram:

1. Vector Search Tool – Provides access to the vector database for the LLM.
2. Director Extraction Tool – Extracts director names from the previously stored last portion of SEC filings.
3. Web Search Tool – Conducts Google searches for directors one at a time and retrieves their LinkedIn handles.

For further details on the code implementation, please refer to the Colab notebook. However, before diving into the notebook, we strongly recommend reviewing the ReAct explanation provided below.

ReAct prompting (Fundamental pattern for AI Agents)

ReAct (Reasoning + Action) represents a groundbreaking framework that revolutionizes how large language models (LLMs) approach complex problem-solving. By combining reasoning capabilities with action-based decision making, ReAct enables models to tackle challenging tasks through a dynamic and iterative process. At its core, the framework establishes a continuous loop of three interconnected components: reasoning, action, and observation.

The reasoning phase, often called the "Thought" stage, serves as the model's internal cognitive process. During this stage, the model analyzes the current situation, drawing upon multiple sources of information including the original task requirements, previous reasoning steps, past actions, and accumulated observations. This framework allows the model to break down complex goals into manageable subtasks, incorporate relevant background knowledge, and continuously evaluate progress toward the ultimate objective. The model can also use this phase to identify potential obstacles and develop contingency plans when faced with unexpected challenges.

The action phase represents the bridge between thought and implementation. It determines which tool to employ based on the preceding thought process. The model examines its available tool descriptions and capabilities, matching them against the requirements identified in its last reasoning step. For example, if the thought process concludes that numerical data needs analysis, the model might select a calculator tool. If the reasoning indicates a need for external information, it might choose a search tool. 

Following each action, the observation phase captures the results and consequences of the actions. These observations serve as crucial feedback, providing new information that feeds into the next iteration of reasoning. For instance, if the model uses a search tool to gather information about a topic, the search results become observations that inform its subsequent thinking and decision-making process. It creates a feedback loop where each cycle of thought, action, and observation builds upon previous iterations. This allows the model to maintain and adjust its high-level strategy while incorporating new information and responding to changing circumstances. The framework's flexibility enables it to handle complex tasks that require multiple steps, logical reasoning, and interaction with various external tools and information sources.

Here is a typical prompt that is used in LangChain framework to implement ReAct:

Your task is to gather relevant information to build context for the question. Focus on collecting details related to the question.

Gather as much context as possible before formulating your answer.

You have access to the following tools:
{tools}

Use the following format:

Question: the input question you must answer

Thought: you should always think about what to do

Action: the action to take, should be one of [{tool_names}]

Action Input: the input to the action

Observation: the result of the action

... (this Thought/Action/Action Input/Observation can repeat N times)

Thought: I now know the final answer

Final Answer: the final answer to the original input question

Begin!

Question: {input}

Thought:{agent_scratchpad}

ReAct implementation: 

• LangChain link
• LLamaIndex link

The prompt starts by defining the task scope and available tools. {tools} is a placeholder that gets populated with descriptions of tools the agent can use, like search, calculators, or data analysis tools.

The format section establishes the strict protocol the agent must follow:

1. Question: {input}

• {input} gets replaced with the actual user question
• This sets up the goal the agent needs to achieve

1. ReAct Components:

• "Thought:" - Where the agent reasons about what it needs to do
• "Action:" - Limited to the tools listed in {tool_names}
• "Action Input:" - The specific input for the chosen tool
• "Observation:" - Where results from tool usage appear

1. The ... can repeat N times indicates this is an iterative process - the agent can go through multiple cycles of Thought/Action/Observation until it has enough information.
2. Conclusion Format:

• A final "Thought:" declaring the agent has sufficient information
• "Final Answer:" providing the response to the original question

1. {agent_scratchpad}
   The {agent_scratchpad} at the end is particularly important - it acts as a dynamic working memory space for the LLM agent and gets populated with the ongoing history of all previous Thought/Action/Observation cycles during execution. Think of it like a digital notepad where the agent records its step-by-step problem-solving process. The scratchpad typically contains:

• Previous thoughts the agent has had including any intermediate conclusions 
• Actions it has taken
• Observations it has received

This allows the agent to:

• Reference previous findings
• Build upon earlier observations
• Maintain continuity in its reasoning process
• Track what approaches have already been tried

Technical Details

It will be worth going over the example covered in the original paper briefly and further understand how ReAct is different from Chain of Thought (CoT) prompting. 

Source: Paper link

The use of few-shot exemplars significantly enhances the efficacy of ReAct. In the original paper, the authors provided the language model with a small number of human-annotated examples that showcase the desired reasoning process and action sequence. These exemplars serve as a template for the model to follow when addressing new, unseen instances of the task.

The exemplars in ReAct typically consist of a series of thought-action-observation steps:

1. Thoughts: The exemplars include explicit reasoning steps that guide the model's decision-making process. These thoughts help break down the task into smaller sub-goals, provide relevant context or common sense knowledge, and offer guidance on the next action to take.
2. Actions: The exemplars demonstrate the specific actions the model should take to progress towards solving the task. These actions can include information retrieval (e.g., searching a knowledge base), navigation (e.g., clicking on a specific link), or providing a final answer.
3. Observations: After each action, the exemplars include the corresponding observation or result from the environment. These observations provide the model with the necessary context to inform its subsequent reasoning and actions.

By studying these few-shot exemplars, the language model learns to internalize the reasoning process and action sequence required to complete the task successfully. The model can then apply this learned pattern to new, unseen instances of the task, even with limited or no additional training.

The ReAct paper demonstrates the effectiveness of this few-shot approach across various domains, including question answering (HotpotQA), fact verification (Fever), and interactive problem-solving (ALFWorld and WebShop). In each case, the model is provided with just a handful of annotated exemplars (ranging from 2 to 6) and achieves competitive performance compared to baseline methods that rely on extensive fine-tuning or reinforcement learning.

How is ReAct different from CoT?

Chain-of-Thought prompting encourages models to break down complex problems into smaller, logical steps before reaching a conclusion. While this approach improves accuracy for many tasks, it operates within the confined space of the model's existing knowledge.

ReAct fundamentally extends the CoT paradigm by introducing dynamic interaction with the external world. While CoT might reason "To find the population of Tokyo, I need to recall the most recent census data," ReAct can actually execute this step by searching current databases. This ability to ground reasoning in real-world data and tools addresses several key limitations of CoT:

• Knowledge Freshness: While CoT relies on the model's training data, ReAct can access current information through external tools.
• Verification Capability: CoT's conclusions are based solely on internal reasoning, but ReAct can verify its assumptions against external sources.
• Computational Accuracy: Rather than relying on the model's ability to perform calculations mentally (as in CoT), ReAct can utilize specialized tools for precise computations.
• Adaptive Problem-Solving: ReAct can adjust its approach based on intermediate results, while CoT follows a more linear reasoning path.

For example, in solving a math problem, CoT might think through each step mentally, while ReAct could combine reasoning with actual calculator usage, reducing computational errors while maintaining logical clarity. This integration of external tools with reasoning creates a more robust and reliable problem-solving system.

Applications and Implications

• Question Answering: ReAct prompting can be used to improve question-answering systems by allowing the model to generate verbal reasoning traces and perform task-specific actions, leading to more accurate and context-aware responses
• Deciding appropriate tools for a sub-task: ReAct prompting can be tailored for wide variety of tasks where the LLM needs to perform actions, such as retrieving specific data, performing computations, or even interacting with software interfaces through APIs.

Challenges and Considerations

• ReAct can easily derail from the main task and pursue self-created tasks not aligned with the original goal.
• ReAct tends to use external tools more often when it can use LLM's knowledge to answer things. 
• Implementing ReAct prompting may require a significant number of prompts, leading to increased costs and potential delays in obtaining the final answer. 
• Complexity in Implementation: Implementing ReAct prompting requires a more complex setup than traditional prompting methods. It involves configuring the LLM to interact with external tools and ensuring secure and efficient communication between the model and these tools.

Process

1. User query

A user asks a question:

Who are the directors of Tesla? What are their LinkedIn handles? What are the financial goals of Tesla this year? What is the next auto show that Tesla will participate in?

2. LLM call to figure out the next step

Langchain's AgentExecutor first generates prompt by filling in the values for placeholder variables {Tools}, {Question}, and {Scratchpad} in the prompt. The prompt becomes like this:

Your task is to gather relevant information to build context for the question. Focus on collecting details related to the question.


Gather as much context as possible before formulating your answer.

You have access to the following tools:
Company Directors Information - Retrieve the names of company directors for a chosen company. Optionally, their LinkedIn handles can also be included. Use the format: company_name, true/false. Available companies: Tesla, General Motors
WebSearch - Performs a web search on the query.
Vector Reranker Search - Retrieves information from an embedding based vector DB containing financial data and company information. Structure query as a sentence

Use the following format:

Question: the input question you must answer

Thought: you should always think about what to do

Action: the action to take, should be one of [Company Directors Information, WebSearch, Vector Reranker Search]

Action Input: the input to the action

Observation: the result of the action

... (this Thought/Action/Action Input/Observation can repeat N times)

Thought: I now know the final answer

Final Answer: the final answer to the question.

Follow these steps:

Begin!

Question: Who are the directors of Tesla. What are their linkedin handles? What are the financial goals of tesla this year. What is the next auto show that Tesla will participate in.

Thought:

After generating the prompt it sends the generated prompt to LLM, parses its response, and add the response to scratchpad in the following format:

Thought: To answer the question, I need to gather information on the directors of Tesla, their LinkedIn handles, Tesla's financial goals for this year, and the next auto show Tesla will participate in.

First, I will retrieve the names of the company directors for Tesla and their LinkedIn handles.

Action: Company Directors Information
Action Input: Tesla, true

Here:

• The Thought explains the reasoning and identifies the needed information.
• The Action specifies the name of the external tool that can help with the needed information in Thought.

The Action Input tells the system what specific data is needed by this external tool.

3. Function calling

AgentExecutor executes the action using the tool suggested by LLM in its last response. It retrieves the response generated by the tool, which is Company Directors Information tool and adds the response as Observation to the scratchpad.

Thought: To answer the question, I need to gather information on the directors of Tesla, their LinkedIn handles, Tesla's financial goals for this year, and the next auto show Tesla will participate in.

First, I will retrieve the names of the company directors for Tesla and their LinkedIn handles.

Action: Company Directors Information
Action Input: Tesla, true

Observation: Directors of Tesla: Elon Musk (LinkedIn: https://www.linkedin.com/in/elon-musk-a93a0b221); Robyn Denholm (LinkedIn: 
…
https://www.linkedin.com/in/kathleen-wilson-thompson-275654201)
Thought:

NOTE: The ellipses ("...") in this document indicate the information has been redacted to conserve space and it can be seen in Langsmith's interface

4. LLM call to figure out the next step

AgentExecutor updates the value of scratchpad in the prompt, and sends it again to LLM for it to decide the next step. It again parses the response received from LLM, and adds the parsed response (Thought, Action and Action input) to scratchpad:

Thought: To answer the question, I need to gather information on the directors of Tesla, their LinkedIn handles, Tesla's financial goals for this year, and the next auto show Tesla will participate in.

First, I will retrieve the names of the company directors for Tesla and their LinkedIn handles.

Action: Company Directors Information
Action Input: Tesla, true

Observation:  Directors of Tesla: Elon Musk (LinkedIn: https://www.linkedin.com/in/elon-musk-a93a0b221); Robyn Denholm (LinkedIn: 
…
https://www.linkedin.com/in/kathleen-wilson-thompson-275654201)
Thought:I have obtained the names and LinkedIn handles of the directors of Tesla. Next, I will gather information on Tesla's financial goals for this year.

Action: Vector Reranker Search
Action Input: What are the financial goals of Tesla for this year?

5. Function calling

AgentExecutor executes the action using the tool suggested by LLM in its last response. It retrieves the response generated by the tool, which is Vector Reranker Search tool and adds the response as Observation to the scratchpad. The response in this case consists of retrieved reranked chunks from vector database for the query What are the financial goals of Tesla for this year?

Thought: To answer the question, I need to gather information on the directors of Tesla, their LinkedIn handles, Tesla's financial goals for this year, and the next auto show Tesla will participate in.

First, I will retrieve the names of the company directors for Tesla and their LinkedIn handles.

Action: Company Directors Information
Action Input: Tesla, true

Observation: Directors of Tesla: Elon Musk (LinkedIn: https://www.linkedin.com/in/elon-musk-a93a0b221); Robyn Denholm (LinkedIn: 
…
https://www.linkedin.com/in/kathleen-wilson-thompson-275654201)
Thought:I have obtained the names and LinkedIn handles of the directors of Tesla. Next, I will gather information on Tesla's financial goals for this year.

Action: Vector Reranker Search
Action Input: What are the financial goals of Tesla for this year?

Observation: required to spend RMB 14.08 billion in capital expenditures by the end of 2023, which has been achieved in 2023, and to generate RMB 2.23 billion of annual tax revenues 
…
audited the Company's internal control over financial
Thought:

6. LLM call to figure out the next step

AgentExecutor updates the value of scratchpad in the prompt, and sends it again to LLM for it to decide the next step. It again parses the response received from LLM, and adds the parsed response (Thought, Action and Action input) to scratchpad:

Thought: To answer the question, I need to gather information on the directors of Tesla, their LinkedIn handles, Tesla's financial goals for this year, and the next auto show Tesla will participate in.

First, I will retrieve the names of the company directors for Tesla and their LinkedIn handles.

Action: Company Directors Information
Action Input: Tesla, true

Observation: Directors of Tesla: Elon Musk (LinkedIn: https://www.linkedin.com/in/elon-musk-a93a0b221); Robyn Denholm (LinkedIn: 
…
https://www.linkedin.com/in/kathleen-wilson-thompson-275654201)
Thought:I have obtained the names and LinkedIn handles of the directors of Tesla. Next, I will gather information on Tesla's financial goals for this year.

Action: Vector Reranker Search
Action Input: What are the financial goals of Tesla for this year?

Observation: required to spend RMB 14.08 billion in capital expenditures by the end of 2023, which has been achieved in 2023, and to generate RMB 2.23 billion of annual tax revenues 
…
audited the Company's internal control over financial
Thought:I have gathered information on Tesla's financial goals for this year, which include capital expenditures and tax revenue generation. Now, I will search for information on the next auto show that Tesla will participate in.

Action: WebSearch
Action Input: Next auto show Tesla will participate in 2023

The LLM determines that the newly obtained information from Vector Reranker Search aligns with its financial goals and identifies the remaining need as finding details about the next auto show using the Websearch tool.

7. Function calling

AgentExecutor executes the action using the tool suggested by LLM in its last response. It retrieves the response generated by the tool, which is WebSearch tool and adds the response as Observation to the scratchpad. The response in this case consists of top 3 Google search results for the query: Next auto show Tesla will participate in 2023

…
Thought:I have gathered information on Tesla's financial goals for this year, which include capital expenditures and tax revenue generation. Now, I will search for information on the next auto show that Tesla will participate in.

Action: WebSearch
Action Input: Next auto show Tesla will participate in 2023

Observation: Title: Tesla to Join 2023 Detroit Auto Show
Snippet: Tesla will join the 2023 Detroit Auto Show that will take place September 13-24. The company will showcase its vehicles at the Powering ...
Link: https://driveteslacanada.ca/news/tesla-to-join-2023-detroit-auto-show/
…
Link: https://detroitautoshow.com/
Thought:

8. LLM call to figure out the next step

AgentExecutor updates the value of scratchpad in the prompt, and sends it again to LLM for it to decide the next step. It again parses the response received from LLM, and adds the parsed response. But this time, the parse response is different. Instead of generating Action and Action-input, it generates the Final answer, since the LLM observes that it has all the information it needed to answer user's question.

Thought: I have gathered the necessary information to answer the question.

Final Answer: 

1. Directors of Tesla and their LinkedIn handles:
   - Elon Musk (LinkedIn: [Elon Musk](https://www.linkedin.com/in/elon-musk-a93a0b221))
   - Robyn Denholm (LinkedIn: [Robyn Denholm](https://au.linkedin.com/in/robyn-denholm-a807795))
   - Ira Ehrenpreis (LinkedIn: [Ira Ehrenpreis](https://www.linkedin.com/in/iraehrenpreis))
   - Joseph Gebbia (LinkedIn: [Joseph Gebbia](https://www.linkedin.com/in/john-coppola-jr-4b2ab395))
   - James Murdoch (LinkedIn: [James Murdoch](https://www.linkedin.com/in/jamesrmurdoch))
   - Kimbal Musk (LinkedIn: [Kimbal Musk](https://www.linkedin.com/in/kimbal))
   - JB Straubel (LinkedIn: [JB Straubel](https://www.linkedin.com/in/jb-straubel-b694981))
   - Kathleen Wilson-Thompson (LinkedIn: [Kathleen Wilson-Thompson](https://www.linkedin.com/in/kathleen-wilson-thompson-275654201))

2. Financial goals of Tesla for this year include capital expenditures of RMB 14.08 billion and generating RMB 2.23 billion in annual tax revenues by the end of 2023. Tesla aims to improve vehicle performance, decrease production costs, and increase affordability and customer awareness.

3. The next auto show Tesla will participate in is the 2023 Detroit Auto Show, which takes place from September 13-24.

Reflexion

Reflexion is a framework that enables language models (LLMs) to learn and improve their performance on various tasks through self-reflection and iterative optimization. The key idea behind Reflexion is to convert binary or scalar feedback from the environment into informative verbal feedback, which the LLM agent then uses to update its context and improve its performance in subsequent attempts.

The Reflexion framework consists of four main components:

• Actor: An LLM that generates text and actions based on the current state observations and its memory. The Actor samples actions from its current policy and receives observations from the environment. Various models, such as Chain of Thought and ReAct, can be used as the Actor.
• Evaluator: A component that assesses the quality of the generated outputs produced by the Actor. The Evaluator takes a generated trajectory as input and computes a reward score reflecting the Actor's performance on the given task. The Evaluator can incorporate both internal and external assessment mechanisms. Internal evaluation can be using self-reflection or confidence signals like log probabilities and entropy measures that assess output quality without external reference points. External evaluation involves independent validation through unit tests, searching the web for relevant information to fact-check, or using LLM-as-a-judge approaches that provide assessment based on predefined criteria. 
   
• Self-Reflection: An LLM that generates verbal self-reflections to provide feedback for future trials. Given the current trajectory, evaluation and the agent's persistent memory, the Self-Reflection model generates specific and informative feedback. This feedback is stored in the agent's memory for future reference.
• Memory: The memory component in Reflexion consists of short-term memory (trajectory history) and long-term memory (outputs from the Self-Reflection model). These memory components provide context that is both specific and influenced by lessons learned over multiple trials, giving Reflexion agents an advantage over other LLM action-choice methods. The Actor uses the updated memory to inform its decisions in the next trial.

Source: Paper link

Reflection implementation: LlamaIndex link

This iterative process of trial, evaluation, self-reflection, and memory persistence allows the agent to rapidly improve its performance on various tasks. This approach draws inspiration from human cognitive processes, particularly the distinction between "System 1" and "System 2" thinking patterns first popularized by psychologists. System 1 represents quick, instinctive reactions, while System 2 embodies slower, more deliberate analysis. While this additional computational step may increase response time, it often proves valuable for complex tasks where accuracy and thoroughness matter more than speed.

Reflection:

Reflection is a simpler version of Reflexion where there is no separate evaluation before self-critique. The reflector simply provides feedback based on its own assessment, which can be helpful but might not address specific shortcomings or gaps. At its core, reflection serves as a prompting strategy that enables AI systems to evaluate and refine their responses through structured self-criticism. 

Multi-Agent Collaboration: The Reflection pattern can be implemented in a multi-agent framework, where one agent is responsible for generating outputs and another agent provides constructive criticism. This back-and-forth between the agents can lead to increasingly refined and improved responses.

Sample implementation:

"Review your previous answer and find problems with your answer"

"Based on the problems you found, improve your answer."

Source: link

Process

1. User query

A user asks a question:

Who are the directors of Tesla? What are their LinkedIn handles? What are the financial goals of Tesla this year? What is the next auto show that Tesla will participate in?

2. LLM call to figure out the next step + Function calling + …

Langchain's AgentExecutor first generates prompt by filling in the values for placeholder variables {Tools}, {Question}, and {Scratchpad} in the prompt.Then it sends the prompt to LLM, parses the output, calls the tool as instructed by the LLM, receives the tool's output, updates the output as observation in scratchpad, and then sends the prompt back to LLM. This process continues for a bit, till it gets to the following point.

Your task is to gather relevant information to build context for the question. Focus on collecting details related to the question.
    Gather as much context as possible before formulating your answer.

    You have access to the following tools:

    Company Directors Information - Retrieve the names of company directors for a chosen company. Optionally, their LinkedIn handles can also be included. Use the format: company_name, true/false. Available companies: Tesla, General Motors
WebSearch - Performs a web search on the query.
Vector Reranker Search - Retrieves information from an embedding based vector DB containing financial data and company information. Structure query as a sentence
Director's previous work and education - Retrieves director's education and work experience using their LinkedIn URL. Use the format: url

    Use the following format:

    Question: the input question you must answer

    Thought: you should always think about what to do

    Action: the action to take, should be one of [Company Directors Information, WebSearch, Vector Reranker Search, Director's previous work and education]

    Action Input: the input to the action

    Observation: the result of the action

    ... (this Thought/Action/Action Input/Observation can repeat N times)

    Thought: I now know the final answer

    Final Answer: the final answer to the question.

    Follow these steps:

    Begin!

    Question: Make an analysis on the independence of backgrounds of directors at tesla

    Thought:To analyze the independence of backgrounds of directors at Tesla, I need to gather information about the directors, including their names, previous work experience, and education. This will help determine the diversity and independence of their backgrounds.

Action: Company Directors Information
Action Input: Tesla, true

Observation: Directors of Tesla: Elon Musk (LinkedIn: https://www.linkedin.com/in/elon-musk-a93a0b221); Robyn Denholm (LinkedIn: 
…
Thought: To analyze the independence of backgrounds of Tesla's directors, I need to gather information about their previous work experience and education. This will help assess the diversity and independence of their backgrounds.

Action: Director's previous work and education
Action Input: https://www.linkedin.com/in/elon-musk-a93a0b221

Observation: Professional Experience: 
Education: 
Thought: To continue analyzing the independence of backgrounds of Tesla's directors, I need to gather information about the previous work experience and education of the other directors as well.

Action: Director's previous work and education
Action Input: https://au.linkedin.com/in/robyn-denholm-a807795

Observation: Professional Experience: Tech Council of Australia, Tesla, Blackbird, Telstra, Telstra
Education: Australian Institute of Company Directors, UNSW, UNSW Business School
Thought: To continue analyzing the independence of backgrounds of Tesla's directors, I need to gather information about the previous work experience and education of the remaining directors.

Action: Director's previous work and education
Action Input: https://www.linkedin.com/in/iraehrenpreis

Observation: Professional Experience: Menlo Micro, DBL Partners, Tesla Motors, Tesla Motors, Tesla Motors
Education: Stanford University Graduate School of Business, Stanford Law School, UCLA
Thought: To continue analyzing the independence of backgrounds of Tesla's directors, I need to gather information about the previous work experience and education of the remaining directors.

Action: Director's previous work and education
Action Input: https://www.linkedin.com/in/john-coppola-jr-4b2ab395

Observation: Professional Experience: JMC Detailing LLC, Koons Chevrolet, Koons Tyson Jeep, Chili's
Education: Radford University - College of Business and Economics, George C. Marshall High School, marshall high school
Thought: To continue analyzing the independence of backgrounds of Tesla's directors, I need to gather information about the previous work experience and education of the remaining directors.

Action: Director's previous work and education
Action Input: https://www.linkedin.com/in/jamesrmurdoch

Observation: Professional Experience: Lupa Systems, 21st Century Fox, 21st Century Fox, News America Incorporated, News America Incorporated
Education: Harvard University
Thought: To continue analyzing the independence of backgrounds of Tesla's directors, I need to gather information about the previous work experience and education of the remaining directors.

Action: Director's previous work and education
Action Input: https://www.linkedin.com/in/kimbal

Observation: Professional Experience: The Kitchen Community, The Kitchen, Tesla Motors, SpaceX, Me.dium
Education: The French Culinary Institute, Queen's University
Thought: To continue analyzing the independence of backgrounds of Tesla's directors, I need to gather information about the previous work experience and education of the remaining directors.

Action: Director's previous work and education
Action Input: https://www.linkedin.com/in/jb-straubel-b694981

Observation: Professional Experience: Redwood Materials, QuantumScape, Straubel Foundation, Tesla, Volacom
Education: Stanford University, Stanford University, EDGEWOOD HIGH SCHOOL
Thought: To complete the analysis on the independence of backgrounds of Tesla's directors, I need to gather information about the previous work experience and education of the last remaining director, Kathleen Wilson-Thompson.

Action: Director's previous work and education
Action Input: https://www.linkedin.com/in/kathleen-wilson-thompson-275654201

Observation: Professional Experience: McKesson, Self-employed, Tesla, Tesla, Walgreens Boots Alliance
Education: University of Michigan, Wayne State University, Wayne State University Law School
Thought:

3. LLM call to figure out the next step

…

Observation: Professional Experience: McKesson, Self-employed, Tesla, Tesla, Walgreens Boots Alliance
Education: University of Michigan, Wayne State University, Wayne State University Law School
Thought: To analyze the independence of backgrounds of Tesla's directors, I have gathered information about their previous work experience and education. Here's a summary of the findings:

1. **Elon Musk**: Information on his professional experience and education was not retrieved, but he is widely known as the CEO of Tesla, SpaceX, and other ventures.

2. **Robyn Denholm**: Has experience in telecommunications and technology sectors, having worked at Telstra and the Tech Council of Australia. She holds degrees from UNSW and the Australian Institute of Company Directors.

3. **Ira Ehrenpreis**: Has a background in venture capital and law, with experience at DBL Partners and education from Stanford University and UCLA.

4. **Joseph Gebbia**: His professional experience includes roles in the automotive and hospitality industries, with education from Radford University.

5. **James Murdoch**: Comes from a media and entertainment background, having worked at 21st Century Fox and News America Incorporated. He is a Harvard University graduate.

6. **Kimbal Musk**: Has experience in the culinary and technology sectors, with roles at The Kitchen and SpaceX. He studied at The French Culinary Institute and Queen's University.

7. **JB Straubel**: Has a strong background in technology and sustainability, with experience at Tesla and Redwood Materials. He studied at Stanford University.

8. **Kathleen Wilson-Thompson**: Has experience in healthcare and retail, having worked at Walgreens Boots Alliance and McKesson. She holds degrees from the University of Michigan and Wayne State University Law School.

The directors of Tesla have diverse backgrounds spanning technology, telecommunications, media, culinary arts, venture capital, and healthcare. This diversity suggests a level of independence in their backgrounds, as they bring varied experiences and perspectives to the board. 

Final Answer: Tesla's board of directors has diverse and independent backgrounds, with experience in technology, telecommunications, media, culinary arts, venture capital, and healthcare, contributing varied perspectives to the company.

4. Prompt for Reflection

Your task is to analyze whether the `Answer` is missing some information related to the `Question`.
    Give feedback on the missing requirments of the answer. Mention only the essential information.

    Here is the previous interaction:
    Question: Make an analysis on the independence of backgrounds of directors at tesla
    Answer: Tesla's board of directors has diverse and independent backgrounds, with experience in technology, telecommunications, media, culinary arts, venture capital, and healthcare, contributing varied perspectives to the company.

    Reflection:
    Provide brief, concise thoughts on what additional information needs to be collected in the next iteration.

    Based on your reflection, conclude with one of the following actions:

    If the current Answer provides sufficient information for Original Input, state "STOP".
    If further refinement is needed, provide 2-3 brief thoughts for improvement, each on a new line, and end with "CONTINUE".

Output

5. Reflection assessment

…
Output The answer provides a general overview of the backgrounds of Tesla's directors, highlighting their diverse experiences and educational backgrounds. However, it lacks specific analysis on the independence of these backgrounds in terms of decision-making and influence within the board. 

- Include an analysis of how the diverse backgrounds contribute to independent decision-making and governance at Tesla.
- Discuss any potential conflicts of interest or affiliations that might affect the independence of the directors.
- Provide examples or evidence of how the directors' independent backgrounds have influenced Tesla's strategic decisions or policies.

CONTINUE

6. Prompt for thinking on Reflection 

Improve `Previous Answer` based on `Reflections`. Dont look for information already present in `Previous Answer`.
    Formulate a new Final Answer.

    Reflections: The answer provides a general overview of the diversity and independence of Tesla's directors' backgrounds. However, it lacks specific details and analysis.

- Include specific examples of directors and their backgrounds to illustrate the diversity and independence.
- Analyze how these diverse backgrounds impact decision-making and governance at Tesla.
- Discuss any potential challenges or benefits that arise from having such a diverse board.

CONTINUE

7. Response to Reflection

 Previous Answer: Tesla's directors have diverse and independent backgrounds, with experiences spanning technology, automotive, media, culinary, and healthcare industries, and education from prestigious institutions. This diversity can contribute to a broad range of perspectives and expertise in the boardroom.
   
The directors of Tesla have diverse and independent backgrounds, which contribute to a broad range of experiences and perspectives within the board. Here's a summary of their backgrounds:

1. **Elon Musk**: Holds bachelor's degrees in physics and economics and attended graduate school in physics at Stanford University. He has a strong background in technology and entrepreneurship, having founded companies like SpaceX and Tesla.

2. **Robyn Denholm**: Has experience in telecommunications and technology, having worked at Telstra and Blackbird. She studied at the Australian Institute of Company Directors and UNSW.

3. **Ira Ehrenpreis**: Has a background in venture capital and law, with education from Stanford University Graduate School of Business and Stanford Law School.

4. **Joseph Gebbia**: His professional experience includes roles in the automotive and hospitality industries, with education from Radford University.

5. **James Murdoch**: Comes from a media and entertainment background, having worked at 21st Century Fox and News America Incorporated. He studied at Harvard University.

6. **Kimbal Musk**: Has experience in the culinary and technology sectors, with education from The French Culinary Institute and Queen's University.

7. **JB Straubel**: Has a strong background in engineering and technology, having worked at Tesla and founded Redwood Materials. He studied at Stanford University.

8. **Kathleen Wilson-Thompson**: Has experience in healthcare and law, having worked at Walgreens Boots Alliance and McKesson. She studied at the University of Michigan and Wayne State University Law School.

Overall, the directors bring a mix of expertise from technology, media, law, healthcare, and entrepreneurship, which supports a diverse and independent board at Tesla.