Why AI Agents Can't Read Your Documentation (And How to Fix It)

A developer just asked Claude how to use your API. Claude made something up. Your docs exist, but AI can't find them.

Here's why — and how to fix it in under an hour.

The Invisible Docs Problem#

Your documentation might rank #1 on Google. But that doesn't matter to AI agents.

When developers use Cursor, Claude, or ChatGPT to write code, these AI assistants:

1. Can't browse the web in most contexts
2. Rely on training data that's months or years old
3. Have no way to verify information against your current docs

The result? AI either hallucinates incorrect information or admits it doesn't know.

Meanwhile, your competitor with AI-readable docs gets recommended instead.

3 Reasons AI Can't Read Your Docs#

1. No Machine-Readable Index#

AI agents need a simple, parseable map of your documentation. Without one, they're navigating blind.

The solution: llms.txt — a plain text file that indexes your docs for AI consumption.

# Your Product
 
> A brief description of what your product does.
 
## Getting Started
 
- [Installation](https://docs.yoursite.com/install): How to install the SDK
- [Authentication](https://docs.yoursite.com/auth): Set up API keys
 
## API Reference
 
- [Users](https://docs.yoursite.com/api/users): User management endpoints
- [Orders](https://docs.yoursite.com/api/orders): Order processing endpoints

Place this at yoursite.com/llms.txt. AI agents can now understand your documentation structure.

2. JavaScript-Rendered Content#

Many documentation platforms render content using JavaScript. The problem? AI can't execute JavaScript.

Symptoms of this issue:

• Your docs look great in a browser
• But viewing page source shows mostly <script> tags
• Minimal actual content in the HTML

The solution: Use a platform that generates static HTML or server-renders content. AI agents (and search engines) can only parse what's in the HTML.

3. No Structured Content#

AI agents understand content hierarchically. Unstructured docs are hard to parse.

Bad structure:

Here's how authentication works. You need an API key.
Get one from the dashboard. Then use it like this...

Good structure:

## Authentication
 
### Overview
API requests require authentication using API keys.
 
### Getting an API Key
1. Log into the dashboard
2. Navigate to Settings → API Keys
3. Click "Generate New Key"
 
### Using Your API Key
Include the key in the Authorization header:

Clear headings help AI understand context and relationships.

The 1-Hour Fix#

Here's a prioritized action plan:

Minute 0-15: Create llms.txt#

1. Go to dokly.co/tools/llms-txt-generator
2. Enter your site name and description
3. Add your documentation pages (or import from sitemap)
4. Download the file
5. Upload to your site's root directory

Minute 15-30: Audit Your Content Structure#

Open your 5 most important documentation pages. Check:

• Page has a clear H1 title
• Sections use H2 headings
• Subsections use H3 headings
• Code examples are complete and runnable
• Steps are numbered, not buried in paragraphs

Fix the most critical issues.

Minute 30-45: Check Server Rendering#

View the source of your documentation (right-click → View Page Source).

Good sign: You see actual content in the HTML Bad sign: Mostly <script> tags with minimal content

If your content is JavaScript-only, consider:

• Switching to a static site generator
• Using a platform like Dokly that renders static HTML
• Adding server-side rendering to your current setup

Minute 45-60: Test with AI#

Open Claude or ChatGPT and ask:

"How do I [common task] with [your product name]?"

Check if the AI:

• Knows about your product
• Provides accurate information
• References your actual documentation

If it fails, your optimization needs more work.

The Companies Already Doing This#

Major developer-focused companies have optimized for AI:

• Anthropic — llms.txt on their docs
• Vercel — AI-readable documentation structure
• Stripe — llms.txt for API reference
• Cloudflare — Structured docs with llms.txt

These companies understand that AI discoverability is the new SEO.

The Competitive Moat#

Here's why this matters long-term:

1. AI training data is cumulative — The sooner you optimize, the more AI models will reference your correct information
2. MCP servers are coming — Model Context Protocol lets AI directly query your docs
3. Developer behavior is shifting — More devs ask AI first, Google second

In 12 months, AI-friendly docs won't be a competitive advantage. They'll be table stakes.

Get AI-Ready Docs Today#

Two options:

DIY approach:

1. Generate your llms.txt (free)
2. Fix your content structure
3. Ensure static HTML rendering
4. Test with AI assistants

Let us handle it: Dokly generates AI-ready documentation automatically. Every docs site includes:

• Auto-generated llms.txt
• Static HTML rendering
• Structured MDX content
• AI writing assistance (BYOK)

Create AI-ready docs — free →

Your competitors are already optimizing for AI. The question is: will developers find you, or them?