The agent line keeps going up—and you need to identify what's below it.

2026-06-17 · Source: How I AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, quick

Summary

A process was developed to create a prompt for an agent specializing in answering questions about BrainTrust documentation. This involved compiling a dataset of common user questions, either sourced from existing documentation queries or through auto-generation, which could be uploaded as a CSV file. The agent utilizes the GPT-5.4 Mini model, connected to a BrainTrust MCP server, to process these queries. The development also explored integrating Context 7, a tool designed for indexing documentation, and included testing the model's baseline knowledge by disabling the MCP server. Initial runs demonstrated the agent's ability to generate answers, suggesting a viable approach for enhancing documentation query responses.

Key takeaway

For AI Engineers building documentation agents, you should prioritize creating a robust dataset of actual user questions to fine-tune prompt effectiveness. Consider integrating specialized context providers like Context 7 or an MCP server to enhance accuracy, but also test your chosen LLM's baseline knowledge. This iterative approach ensures your agent delivers precise and relevant answers, improving user experience with your documentation.

Key insights

Developing documentation agents requires curated question datasets and specific model/context configurations.

Principles

• Use real user questions for training.
• Test model's inherent knowledge.
• Integrate external context for accuracy.

Method

Collect documentation questions, create a dataset, develop a basic prompt, select an LLM (e.g., GPT-5.4 Mini), and integrate a context server (e.g., BrainTrust MCP, Context 7).

In practice

• Compile a CSV of common user questions.
• Experiment with different context providers.
• Evaluate model's performance without external context.

Topics

• Documentation Agents
• Prompt Engineering
• GPT-5.4 Mini
• LLM Context
• BrainTrust MCP
• Context 7

Best for: AI Engineer, NLP Engineer, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by How I AI.