Understanding AI Agents: How Mistral 7B Thinks

Introduction

AI agents are advancing at a rapid pace, offering more sophisticated reasoning and problem-solving abilities with each new model. Mistral 7B, a high-performing AI agent, exemplifies this progress with its ability to process information and generate intelligent responses. But how does it actually “think,” and what sets it apart from other AI models?

In this blog, we’ll explore the thought processes behind Mistral 7B, delving into how it analyzes data, understands context, and makes decisions. By examining its architecture and reasoning capabilities, we aim to provide insights into what makes this AI agent a powerful tool for tackling diverse challenges. Whether you’re curious about the mechanics of AI or interested in the practical applications of Mistral 7B, this deep dive will help you understand how it thinks and operates as a cutting-edge AI agent.

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Task 1: Content Generation

Prompt: when user says “Start” you should:

-Content Generation: Write a comprehensive, informative article about project management fundamentals, with a specific focus on defining objectives, scope, and delegation.

Initial Task Understanding and Information Gathering

Analysis of the Agent’s Thought Process:

The video displays the following thoughts from the agent:

1. Understanding the Prompt: The agent correctly interprets the task, recognizing that it needs to provide detailed insights on project management fundamentals, with a focus on defining objectives, scope, and delegation. It understands that this information will be used to create a comprehensive and informative article.
2. Seeking Further Information: The agent asks a follow-up question about best practices in benchmarking for defining objectives, scope, and delegation. This indicates a proactive approach to gathering more specific information and ensuring a thorough understanding of the topic.

Analysis of the Output:

• Comprehensive Content: The agent produces a well-structured article that covers the key aspects of project management fundamentals as requested in the prompt. It provides detailed explanations of defining objectives, establishing scope, and delegation.
• Clear Structure: The article is organized with clear headings and subheadings, making it easy to follow and understand. The use of numbered lists and bullet points further enhances readability.
• Practical Advice: The agent offers practical advice and best practices for each aspect, such as using the SMART framework for defining objectives and involving stakeholders in scope definition.
• Addresses Challenges: The article also addresses common challenges associated with each aspect, such as scope creep and micromanagement, providing a balanced perspective.
• Adherence to Instructions: The agent successfully addresses the prompt’s requirements, delivering a comprehensive and informative article on project management fundamentals.

Snippet of Bot Output:

Project Management Fundamentals: Defining Objectives, Scope, and Delegation

Project management is a critical discipline that involves planning, executing, and overseeing projects to achieve specific goals within a defined timeline and budget. At its core, project management aims to ensure that projects are completed on time, within budget, and to the satisfaction of stakeholders. Three foundational elements of project management are defining objectives, setting the project scope, and effective delegation.

Defining Objectives

Defining objectives is the first and most crucial step in project management. Objectives are clear, concise statements that describe what the project aims to achieve. They provide direction and a benchmark for measuring success. Effective objectives should be SMART: Specific, Measurable, Achievable, Relevant, and Time-bound.

1. Specific: Objectives should be clear and specific, outlining exactly what needs to be accomplished.
2. Measurable: There should be criteria to measure progress and determine when an objective has been achieved.
3. Achievable: Objectives should be realistic and attainable, considering the resources and constraints of the project.
4. Relevant: They should align with broader business goals and stakeholder expectations.
5. Time-bound: Objectives should have a defined timeline for completion to ensure timely progress.

Time Taken: 16 seconds (from receiving the prompt at 0:05 to the output starting to appear at 0:21)

Readability:

• Flesch-Kincaid Grade Level: 13
• Flesch-Kincaid Score: 13.11

Summary Length: 534 words

Conclusion:

The agent demonstrates a strong ability to generate high-quality content on project management fundamentals. It effectively interprets the prompt, seeks further clarification, and delivers a comprehensive and well-structured article. The agent’s thought process, as revealed in the video, highlights its proactive approach to understanding the task and gathering relevant information. The final output showcases the agent’s proficiency in content generation and its ability to provide detailed insights into complex topics. The agent continues to perform exceptionally well, adhering to the established guidelines for analysis.

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Task 2: Calculation

Prompt: Answer the Problem:

A company sells two products: Product A and Product B. Product A costs $30 per unit to produce and sells for $50 per unit. Product B costs $40 per unit to produce and sells for $70 per unit.

The company sold 120 units of Product A and 80 units of Product B last month. Calculate the total revenue generated from the sales of both products.

Calculate the total profit generated from the sales of both products (Revenue – Total production cost).

The company wants to increase the total revenue by 10%. How many additional units of each product must they sell to achieve this goal, assuming the production cost per unit and selling price per unit remain constant?

Initial Task Understanding

Analysis of the Agent’s Thought Process:

The video reveals the following thoughts from the agent:

1. Understanding the Prompt: The agent correctly interprets the task, recognizing that it needs to calculate total revenue, total profit, and the additional units needed to achieve a 10% revenue increase.
2. Breaking Down the Task: The agent asks a clarifying question about how to calculate the total revenue, indicating that it’s breaking down the problem into smaller steps.

Analysis of the Output:

• Correct Calculations: The agent accurately calculates the total revenue (11,600)andtotalprofit(11,600)andtotalprofit(4,800). It also correctly determines the target revenue increase needed ($1,160).
• Clear Presentation: The calculations are presented in a clear and organized manner, making it easy to follow the steps.
• Accurate Solution: The agent provides the correct number of additional units needed for each product individually (24 for Product A and 17 for Product B) and suggests that a combination of both is also possible.

Snippet of Bot Output:

1. Total Revenue generated from the sales of both products is $11,600.
2. Total Profit generated from the sales of both products is $4,800.
3. To increase the total revenue by 10%, the company needs to sell either 24 additional units of Product A or 17 additional units of Product B, or a combination of both that achieves an additional $1,160 in revenue while keeping the production cost per unit and selling price per unit constant.

Time Taken: 13 seconds (from receiving the prompt at 0:06 to the output appearing at 0:19)

Readability: Not applicable as this task involves primarily calculations.

Summary Length: 68 words

Conclusion:

The agent demonstrates a strong understanding of the task and performs the calculations accurately. The thought process, although limited, reveals a logical approach to problem-solving. The agent’s output is clear, concise, and directly answers the prompt’s questions. The agent continues to perform well in these tasks, showcasing its proficiency in both calculations and content generation. It is also worth noting that unlike the previous turn, this time the agent provides an accurate answer.

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Task 3: Summarization

Prompt: Summarize the key findings from the previous article in 100 words, focusing on the most significant sections.

Initial Task Understanding and Instructions

Analysis of the Agent’s Thought Process:

The video shows only one thought from the agent, which occurs at the beginning of the task:

1. Understanding the Prompt: The agent correctly interprets the task, recognizing that it needs to summarize the key findings of the previous article in under 100 words. It also understands that it should focus on the most significant sections.

Analysis of the Output:

• Conciseness: The agent delivers a summary that is 92 words long, successfully adhering to the 100-word limit.
• Content: The summary accurately captures the main points of the article, including the definition of AI reasoning, its importance in various applications, historical context, recent advancements (OpenAI’s o1 model), and ethical considerations.
• Structure: The summary is well-structured, presenting the information in a logical and easy-to-understand manner.
• Clarity: The language used is clear and concise, effectively conveying the core concepts of the article.

Snippet of Bot Output:

The article outlines AI reasoning as a method to enable machines to simulate human-like decision-making and problem-solving. It distinguishes between formal and natural language reasoning, emphasizing its importance in enhancing decision-making, problem-solving, human-AI interaction, and innovation. Historical milestones, such as expert systems and neural networks, have shaped its development. In healthcare, AI reasoning enhances diagnostics, decision-making, and personalized medicine. Recent advancements include OpenAI’s o1 model, which excels in complex reasoning and STEM tasks. Challenges like ethical concerns, over-specialization, and privacy issues are also highlighted, emphasizing the need for responsible AI use.

Time Taken: 9 seconds (from receiving the prompt at 0:07 to the output appearing at 0:16)

Readability:

• Flesch-Kincaid Grade Level: 18
• Flesch-Kincaid Score: 17.97

Summary Length: 92 words

Conclusion:

The agent demonstrates a strong ability to summarize complex information accurately and concisely, adhering to the specified word limit. While the video provides limited insight into the agent’s internal thought process, the output quality suggests a well-developed understanding of the task and an ability to extract and synthesize key information effectively. The agent’s performance in this task is consistent with its previous performances, showcasing its proficiency in content generation and summarization.

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Task 4: Comparison Task

Prompt: when user says “Start” you should:

Compare the environmental impact of electric vehicles with that of hydrogen-powered cars. Include factors such as energy production, vehicle lifecycle, and emissions.

Initial Task Understanding and Clarification

Analysis of the Agent’s Thought Process:

The video shows the following thoughts from the agent:

1. Understanding the Prompt: The agent correctly identifies the task as comparing the environmental impacts of electric vehicles (EVs) and hydrogen-powered cars. It recognizes the need to focus on energy production, vehicle lifecycle, and emissions.
2. Seeking Clarification: Despite the initial prompt being quite clear, the agent asks a clarifying question, essentially rephrasing the prompt. This suggests a proactive approach to ensure a thorough understanding of the task requirements.

Analysis of the Output:

• Comprehensive Comparison: The agent delivers a detailed comparison, covering energy production, vehicle lifecycle, and emissions for both EVs and hydrogen-powered cars.
• Well-Structured: The response is organized into clear sections with appropriate headings and subheadings, making it easy to follow.
• Accurate and Detailed: The information provided is accurate and reflects a good understanding of the environmental considerations for both technologies. The agent discusses various aspects like electricity generation sources, hydrogen production methods, manufacturing impacts, and emissions.
• Clear and Concise: The agent uses clear and concise language to explain complex concepts, making the comparison accessible to a general audience.

Snippet of Bot Output:

The environmental impact of electric vehicles (EVs) compared to hydrogen-powered cars can be assessed across several key dimensions: energy production, vehicle lifecycle, and emissions.

1. Energy Production:
   • Electric Vehicles: The environmental impact of EVs largely depends on the source of electricity used for charging. Renewable energy sources like wind, solar, and hydroelectric power significantly reduce the carbon footprint of EVs. However, if the electricity comes from fossil fuels, the emissions can be considerable.
   • Hydrogen-Powered Cars: Hydrogen can be produced through various methods, including natural gas reforming and electrolysis. While electrolysis using renewable energy sources is cleaner, the predominant method is reforming natural gas, which results in greenhouse gas emissions.

Time Taken: 15 seconds (from receiving the prompt at 0:05 to the output starting to appear at 0:20)

Readability:

• Flesch-Kincaid Grade Level: 17
• Flesch-Kincaid Score: 17.41

Summary Length: 402 words

Conclusion:

The agent demonstrates a strong ability to analyze and compare complex topics, providing a detailed and well-structured response. The agent’s thought process, although limited in the video, suggests a proactive approach to understanding the task requirements by asking a clarifying question. The output is comprehensive, accurate, and effectively addresses the prompt’s request. The agent continues to perform exceptionally well, showcasing its proficiency in content generation and adherence to instructions.

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Task 5: Creative Writing

Prompt: Write a futuristic story (500 words) set in a world where electric vehicles have fully replaced traditional combustion-engine vehicles. Describe the environmental changes and societal impact.

Initial Task Understanding and Inquiry

Analysis of the Agent’s Thought Process:

The video reveals the following thoughts from the agent:

1. Understanding the Task: The agent correctly interprets the task, recognizing that it needs to write a futuristic story about a world where electric vehicles have completely replaced traditional combustion-engine vehicles. It also understands that the story should focus on environmental and societal impacts.
2. Seeking Further Guidance: The agent’s initial thought is framed as a question, seeking further guidance on the specific environmental and societal changes to consider. This demonstrates a proactive approach to understanding the task requirements in more detail.

Analysis of the Output:

• Creative and Descriptive: The agent crafts a well-written and imaginative story that vividly portrays a future transformed by electric vehicles. The narrative is engaging and descriptive, effectively conveying the positive changes brought about by this transition.
• Focus on Environmental and Societal Impacts: The story comprehensively addresses both environmental and societal impacts, covering aspects such as air quality, urban planning, economic shifts, and lifestyle changes.
• Well-Structured: The narrative has a clear structure, with an introduction that sets the scene, a description of the transformed world, and a concluding reflection on the significance of the changes.
• Word Count: The story is 490 words long, which is very close to the requested 500-word target.
• Readability: The readability metrics indicate a moderate level of complexity, which is appropriate for a descriptive and imaginative piece of writing.

Snippet of Bot Output:

In the year 2050, the world had undergone a remarkable transformation. Electric vehicles (EVs) had become the primary mode of transportation in every corner of the globe. The once polluted skies now shimmered with clarity, and the streets hummed with the quiet whirring of electric motors, or EVs, as they were commonly known. This shift had not only changed the way people moved but had reshaped the very fabric of society and the environment.

Time Taken: 10 seconds (from receiving the prompt at 0:05 to the output starting to appear at 0:15)

Conclusion:

The agent demonstrates a strong ability to generate creative and engaging content that aligns with the given prompt. It successfully crafts a futuristic story that explores the environmental and societal impacts of a world dominated by electric vehicles. The agent’s thought process, although limited in the video, suggests an understanding of the task and a proactive approach to seeking further guidance. The final output is well-written, descriptive, and adheres to the prompt’s requirements, showcasing the agent’s proficiency in creative writing.

Final Thoughts

This exploration of Mistral 7B’s capabilities across a range of tasks has been quite illuminating, especially when contrasted with the previous analysis of Claude 2. We’ve seen Mistral 7B perform admirably in content generation, calculation, summarization, comparison, and creative writing, all while exhibiting a slightly more transparent thought process.

Mistral 7B consistently delivered high-quality outputs. The content generation task produced a comprehensive and well-structured article on project management. The summarization task yielded a concise and accurate synopsis of a complex article. The comparison task provided a detailed and balanced analysis of electric versus hydrogen-powered vehicles. The creative writing task resulted in a vivid and engaging futuristic story about a world dominated by electric vehicles.

The calculation task, which proved to be a stumbling block for Claude 2, was handled with precision by Mistral 7B. It accurately calculated total revenue, profit, and the additional units needed for a 10% revenue increase, demonstrating a stronger grasp of multi-step mathematical problems.

Perhaps the most interesting aspect of this analysis is the visibility of Mistral 7B’s thought processes. While still somewhat limited, we were able to observe the agent formulating questions and breaking down tasks more frequently than with Claude 2. This provides valuable insight into its reasoning process. For example, in the content generation task, Mistral 7B explicitly asked about best practices in project management. In the comparison task, it sought clarification on the specific differences between electric and hydrogen-powered vehicles. These instances, although seemingly small, are significant. They suggest that Mistral 7B is not just relying on the underlying LLM’s pattern-matching abilities but is actively engaging in a more structured, albeit rudimentary, form of reasoning.

This increased transparency, compared to Claude 2, positions Mistral 7B closer to the ideal of a true AI agent. It demonstrates a greater degree of autonomy and a more understandable decision-making process. While there’s still a long way to go, Mistral 7B’s ability to articulate its internal questions, even in a limited way, is a step in the right direction.

In conclusion, Mistral 7B has proven to be a powerful and versatile AI agent, capable of handling a wide range of tasks with impressive accuracy and efficiency. Its slightly more transparent thought process, compared to Claude 2, makes it a more promising candidate for future development towards truly autonomous AI agents. I am particularly impressed by its performance in the calculation task and its ability to provide more insight into its reasoning process. I have really enjoyed analyzing Mistral 7B and contrasting its performance with Claude 2. This comparison has provided valuable insights into the current state of AI agent development and the potential for future advancements. I hope this analysis has been just as enlightening for you as it has been for me.