Overview

Creating an AI persona with B-Bot involves a distinct, methodical approach that prioritizes control and precision. This method ensures that the persona behaves in alignment with desired characteristics and responds accurately to a wide range of queries and tasks. The process can be divided into three key phases: Characteristic Behavior Formation, Informational and Actionable Behavior Training, and Context Optimization.

1. Characteristic Behavior Formation

Objective: Develop a foundational personality and behavior for the AI persona that aligns with the intended use case and user expectations.

Steps:

1. Crafting a System Prompt: The system prompt acts as a "wake word" for the AI persona, serving as a constant anchor for its identity, tone, and purpose throughout all interactions. It is crucial that this prompt remains consistent across all training and fine-tuning activities to ensure stability in the persona’s behavior.

   Crafting a System Prompt

2. Initial Testing: Begin by testing the persona using the system prompt. Ask simple questions such as, "Who are you?" and "What is your name?" The AI’s responses should align closely with the system prompt. If the persona should identify as "B-Bot, a specialized business assistant," but instead responds generically as "an assistant," it indicates a need for further fine-tuning.

   Initial Testing

3. Fine-Tuning with the Hub: Use the fine-tuning feature in the Hub to adjust the AI’s behavior. Rather than altering the system prompt, introduce new training sets that reinforce the desired behavior. These training sets should provide clear examples that guide the AI in how to respond appropriately, based on the fixed system prompt.

   1. Fine-Tuning with the Hub

2. Informational and Actionable Behavior Training

Objective: Equip the AI with the ability to provide accurate information and perform specific actions by integrating relevant tools and knowledge bases.

Steps:

1. Tool Integration: Once the persona’s characteristics are solidified, train the AI to use various tools to gather information or perform tasks. This keeps the context window optimized and ensures the AI remains focused on delivering relevant content.
2. Actionable Training: Train the AI using scenarios where it must decide when to utilize specific tools or access certain knowledge bases, all while maintaining the behavior and tone set by the system prompt.
3. Testing for Relevance: Verify that the AI can effectively gather information or execute tasks with the correct tools, and that it does so in a manner consistent with the established persona.

3. Context Optimization

Objective: Maintain an efficient context window by ensuring the AI operates with only the necessary information for any given task.

Steps:

1. Context Window Management: Regularly monitor how the AI uses its context window during interactions, ensuring that it remains compact and focused on relevant information.
2. Dense Information Training: Fine-tune the AI to prioritize and retain the most critical information, reinforcing this with focused training examples that align with the system prompt.