How to prepare documentation for AI assistants and agents
Make technical documentation easier for AI assistants and agents to retrieve, interpret, and cite without creating a second version of your docs.
AI assistants work better when the documentation they retrieve is current, specific, and easy to interpret.
If an important procedure is missing, two pages contradict each other, or a task depends on context that appears somewhere else, adding another AI integration will not fix the source.
Preparing documentation for AI therefore starts with the same question you would ask for a human reader: can someone find a current page that clearly explains what to do?
This guide covers the documentation changes worth making before you expose your content through a chatbot, AI search, MCP, or another retrieval-based assistant.
Start with the tasks that matter most
You do not need to rewrite your entire documentation set for AI.
Start with important reader tasks and the pages that should support them. Good candidates include:
- getting started with the product
- authenticating an API request
- completing a common configuration
- troubleshooting a frequent error
- migrating from an older workflow
- understanding an important limitation
For each task, check whether the current documentation gives the reader enough information to complete it.
| What you find | What to do |
|---|---|
| No page covers the task | Add the missing documentation |
| A page exists but omits an important condition | Add the prerequisite, limitation, or missing step |
| Several pages contradict each other | Decide which information is current and update or retire the rest |
| The content exists but is difficult to identify | Improve the title, headings, terminology, or linking |
| The task depends on undocumented product behavior | Verify the behavior before documenting it |
This gives you a practical backlog without turning “make the docs AI-ready” into a full-site rewrite.
Make important pages understandable on their own
Readers do not always arrive through your intended navigation sequence, and retrieval systems may surface only the most relevant part of a page.
Important task documentation should therefore include enough context to make sense when someone lands directly on it.
For a procedure, make the important details explicit:
- what the task accomplishes
- who or what the instructions apply to
- prerequisites
- the steps
- the expected result
- important limitations or failure cases
- where to go next
For example:
# Configure API authentication
Use this guide to send authenticated requests to the fictional Acme API.
You need an active Acme account and an API key.
1. Store the API key in your environment configuration.
2. Add the documented `Authorization` header to the request.
3. Send a test request.
4. Confirm that the API returns the expected authenticated response.
If the request returns `401`, verify that the API key is active and that you are using the correct environment.
Next: [Configure webhooks](/docs/webhooks).This structure is useful for humans and makes it easier for retrieval-backed assistants to identify the task, conditions, and expected outcome.
Do not duplicate every prerequisite on every page. Link to maintained supporting material when another page owns the detail, but make the dependency clear.
Remove stale and conflicting instructions
Contradictory documentation creates a difficult problem for any retrieval system.
If one guide describes the current procedure and another still documents the old one, an assistant may retrieve either or both.
Fix the content instead of expecting the model or ranking system to decide which version your team actually supports.
| Problem | Better approach |
|---|---|
| Two pages describe different procedures | Update the canonical documentation and retire or redirect the old version |
| An old product version must remain documented | Label the version clearly |
| Several pages repeat the same instructions | Consolidate the shared procedure or make one page authoritative |
| A page is outdated but still receives traffic | Redirect it when appropriate or clearly point to the replacement |
| Internal content should not inform public answers | Keep it outside the relevant source scope |
A smaller corpus is not automatically better. Keep the coverage readers need, but remove ambiguity where you can.
Write instructions that can be checked
AI-generated explanations can sound convincing even when they omit the detail that determines whether a procedure works.
Make those details explicit in the source.
For technical procedures, check that the documentation names:
| Include | Example |
|---|---|
| Scope | Which product, plan, version, or environment the instructions apply to |
| Prerequisites | What must be true before step one |
| Location | The file, command, API field, or UI control to change |
| Expected result | What confirms that the procedure worked |
| Error or recovery path | What to check when the expected result does not occur |
Examples and commands deserve the same scrutiny.
Verify that code samples use current APIs, configuration names, endpoints, and product behavior. Avoid examples that look complete but silently depend on setup the page never mentions.
Screenshots can support a procedure, but important controls, values, and outcomes should also appear in text.
Use the language your readers use
Retrieval can bridge differences between reader wording and documentation terminology, but clear language still helps.
If readers search or ask about “replacing a leaked API key” while every page only says “credential lifecycle management,” consider whether the documentation should use both terms.
Look at:
- page titles
- headings
- introductory paragraphs
- error messages
- common product synonyms
- terminology used in support and onboarding
Do not stuff pages with alternate keywords for AI. Use natural language that helps a reader recognize that they have reached the right page.
Test realistic questions against the documentation
After improving the source, test the questions readers are likely to ask.
Include more than the exact wording used in the page title.
For example:
| Question type | Example |
|---|---|
| Common task | “How do I rotate an API key?” |
| Alternate wording | “My API key leaked. How do I replace it?” |
| Recently changed task | “Which authentication flow should I use now?” |
| Multi-part task | “How do I rotate the key without breaking my deployment?” |
| Unsupported request | “Can I restore a deleted API key?” |
Check which source the system uses and whether the answer stays within what that documentation supports.
If the correct source exists but is not retrieved, investigate source configuration, structure, or terminology.
If the source itself does not answer the question, fix the documentation first.
For a fuller answer-review method, see how to evaluate a docs chatbot before you ship.
Choose how AI systems access the docs after the source is ready
Once the documentation itself is in good shape, decide how readers and AI tools should access it.
Public documentation, llms.txt, and MCP solve different problems.
A public documentation site gives people and web-based systems a canonical source. An optional llms.txt file can provide a maintained map of useful content. MCP can make documentation available inside compatible AI clients.
None of these access paths repairs weak source content.
For the detailed comparison, see crawlable docs, llms.txt, or MCP: which should you use?.
Use Biel.ai to test your maintained sources
Biel.ai lets you create a project, add documentation sources, index them, and test questions against that source set.
Depending on your use case, project sources can include websites, files, GitHub repositories, OpenAPI specifications, Confluence spaces, and private content.
See the Sources documentation for current source types and configuration options.
Once the sources are indexed, test representative questions in the dashboard before exposing the assistant through chat, MCP, or another supported surface.
The Quickstart covers project creation, source setup, indexing, and testing.
Frequently asked questions
Do I need to rewrite every page for AI assistants?
No. Start with important tasks, pages that regularly cause confusion, and content that is missing, outdated, contradictory, or difficult to identify.
Should every task have its own page?
No. Organize documentation in the way that makes sense for your readers. The important part is that a task has a clear, current source path and enough context to understand what to do.
Can [object Object] fix weak documentation?
No. It can provide a map of maintained content for tools that choose to use it, but it does not make missing or ambiguous instructions reliable.
How can a documentation AI platform reduce hallucinations?
Use a platform that retrieves from maintained documentation, keeps the source visible, and handles questions conservatively when the docs do not support an answer.
Good documentation still matters. Clear, current, non-conflicting sources give the retrieval system better material to work with and make generated answers easier to verify.
What should we test first?
Choose a few important reader tasks and ask them using realistic wording. Check that the correct documentation is retrieved and that the answer does not go beyond what the source supports.
Start with the documentation, not the AI integration
Choose a few important reader tasks and inspect the pages that should support them.
Make the instructions current, remove contradictions, add missing context, and test whether realistic questions reach the right sources.
Then choose the chatbot, search, MCP, or other access path that fits how your readers and tools need to use the documentation.
If you want to test an assistant against your existing docs, create a Biel.ai account and follow the Quickstart to add your sources and ask your first questions.