Building an AI-ready help center
In the Agentic Era, your documentation is no longer just a collection of articles; it is the structured data and context that prevents your AI from hallucinating and ensures it can actually do the work.
Some companies are panicking and making their documentation private to stop AI models from training on it for free. This is a strategic blunder for support. Why?
Many customers will not visit your website; they will send their own personal AI assistants to find answers for them. If your documentation is gated, a customer's personal agent can't read your resolution paths. This forces the customer to manually log in and search, creating immediate friction that leads to high-cost human tickets.
Documentation is now trust infrastructure. When a machine can verify your facts, it recommends your brand. Open docs make your product AI-addressable.
To make your help center usable via the Model Context Protocol (MCP), you need to follow these tactical rules:
“One article, one question” rule
AI models perform significantly better when provided with optimized, focused contexts. Each article should answer exactly one question (for example, “How do I reset my API key?”) to avoid context overload.
Semantic chunking
Structure your content so it can be broken into chunks of 100–300 tokens for speed or 500–1000 tokens for accuracy. Ensure each chunk is self-contained so that, if it is retrieved alone, it still makes sense to the AI.
Repeat context in the body
Don’t rely on the title alone. If the title is “How to process a refund”, repeat that key phrase in the first paragraph. When an AI chunks your article, the title can get separated from the steps, making the steps meaningless to the LLM.
Explicit definitions
Humans use intuition; AI uses explicit data. Stop using vague jargon. Use clear, imperative language (for example, “Click the blue button” instead of “The user might find the submission option useful”).
Text-only fallbacks
AI agents primarily read. Ensure there is a text-only version of every diagram, video, or image, as non-text content will not be indexed by most retrieval-augmented generation (RAG) systems.