RAG Patterns for Code Documentation

Intermediatev1.0.0

Implement Retrieval-Augmented Generation patterns to give AI coding tools access to your documentation, API specs, and codebase knowledge for more accurate code generation.

Content

Overview

RAG (Retrieval-Augmented Generation) enhances AI coding tools by dynamically retrieving relevant documentation, examples, and API specs based on the current task. Instead of cramming everything into the system prompt, RAG fetches only what's needed.

Why This Matters

-Accuracy — AI generates code matching your actual API signatures
-Freshness — documentation updates are reflected immediately
-Scale — works with documentation too large for any context window
-Relevance — only task-related context is included

How It Works

Step 1: Index Your Documentation

bash

# Using a documentation indexer
# Index API docs, README files, and code examples
npx @docs-indexer/cli index \
  --source ./docs \
  --source ./README.md \
  --source ./src/types \
  --output ./.docs-index

Step 2: Chunk and Embed

typescript

// Chunk documentation into retrieval-friendly segments
interface DocChunk {
  content: string;      // The text content
  source: string;       // File path
  section: string;      // Section heading
  embedding: number[];  // Vector embedding
}

// Chunk by section headings for API docs
// Chunk by function/class for code
// Chunk by paragraph for prose documentation

Step 3: Retrieve on Query

typescript

// When AI needs context, retrieve relevant chunks
async function getRelevantDocs(query: string, topK: number = 5) {
  const queryEmbedding = await embed(query);
  const results = await vectorDB.search(queryEmbedding, topK);
  return results.map(r => r.content).join('\n\n');
}

Step 4: Augment the Prompt

typescript

const context = await getRelevantDocs("create user API endpoint");
const prompt = `
Based on the following documentation:
---
${context}
---

Generate a new API endpoint for user creation following the documented patterns.
`;

RAG for AI Coding Tools

Cursor: @docs Integration

code

# Cursor supports @docs for RAG
# Index documentation URLs in Cursor settings
# Reference in chat: @docs how does the auth middleware work?

Claude Code: MCP + RAG

json

{
  "mcpServers": {
    "docs": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "./docs"]
    }
  }
}

Best Practices

-Chunk by logical units (functions, API endpoints, sections) not arbitrary size
-Include code examples in your indexed documentation
-Re-index when documentation changes (add to CI pipeline)
-Test retrieval quality — relevant chunks should appear in top 5 results
-Combine RAG with system prompts for best results

Common Mistakes

-Indexing too much (node_modules, generated files) — noise in results
-Chunks too large (> 1000 tokens) or too small (< 50 tokens)
-Not updating the index when docs change
-Relying only on RAG without system prompt constraints
-Not testing retrieval quality before deploying

FAQ

Discussion

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RAG Patterns for Code Documentation

Intermediatev1.0.0

Implement Retrieval-Augmented Generation patterns to give AI coding tools access to your documentation, API specs, and codebase knowledge for more accurate code generation.

Content

Overview

Why This Matters

-Accuracy — AI generates code matching your actual API signatures
-Freshness — documentation updates are reflected immediately
-Scale — works with documentation too large for any context window
-Relevance — only task-related context is included

How It Works

Step 1: Index Your Documentation

bash

# Using a documentation indexer
# Index API docs, README files, and code examples
npx @docs-indexer/cli index \
  --source ./docs \
  --source ./README.md \
  --source ./src/types \
  --output ./.docs-index

Step 2: Chunk and Embed

typescript

// Chunk documentation into retrieval-friendly segments
interface DocChunk {
  content: string;      // The text content
  source: string;       // File path
  section: string;      // Section heading
  embedding: number[];  // Vector embedding
}

// Chunk by section headings for API docs
// Chunk by function/class for code
// Chunk by paragraph for prose documentation

Step 3: Retrieve on Query

typescript

// When AI needs context, retrieve relevant chunks
async function getRelevantDocs(query: string, topK: number = 5) {
  const queryEmbedding = await embed(query);
  const results = await vectorDB.search(queryEmbedding, topK);
  return results.map(r => r.content).join('\n\n');
}

Step 4: Augment the Prompt

typescript

const context = await getRelevantDocs("create user API endpoint");
const prompt = `
Based on the following documentation:
---
${context}
---

Generate a new API endpoint for user creation following the documented patterns.
`;

RAG for AI Coding Tools

Cursor: @docs Integration

code

# Cursor supports @docs for RAG
# Index documentation URLs in Cursor settings
# Reference in chat: @docs how does the auth middleware work?

Claude Code: MCP + RAG

json

{
  "mcpServers": {
    "docs": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "./docs"]
    }
  }
}

Best Practices

-Chunk by logical units (functions, API endpoints, sections) not arbitrary size
-Include code examples in your indexed documentation
-Re-index when documentation changes (add to CI pipeline)
-Test retrieval quality — relevant chunks should appear in top 5 results
-Combine RAG with system prompts for best results

Common Mistakes

-Indexing too much (node_modules, generated files) — noise in results
-Chunks too large (> 1000 tokens) or too small (< 50 tokens)
-Not updating the index when docs change
-Relying only on RAG without system prompt constraints
-Not testing retrieval quality before deploying

FAQ

Discussion

Loading comments...