Why Your AI Coding Assistant Is Generating Broken Code (And How ODocs Will Fix It)

TL;DR

AI coding assistants are powerful, but they suffer from a critical flaw: "version blindness." They cannot detect which version of a framework or library you're using, nor access the correct documentation for that version. This results in broken code, wasted debugging time, and frustrating developer experiences.

ODocs is an open-source documentation registry that works like Docker Hub, but for documentation. It enables AI models to detect your framework versions and access the right documentation, ensuring they generate compatible code on the first try.

The Critical Problem: Version-Blind AI

Every AI coding assistant faces the same fundamental limitation: version blindness. Here's a simple example:

1. An AI assistant suggests: npx create-next-app@latest
2. When the AI was trained, "latest" was Next.js 14.0
3. When you run the command today, you get Next.js 15.2
4. The AI then suggests code patterns that worked for 14.0 but are incompatible with 15.2

This version mismatch happens constantly across all programming ecosystems:

• JavaScript/TypeScript: React hooks that worked in 17.0 but throw errors in 18.2
• Python: Django patterns that were best practice in 4.2 but deprecated in 5.0
• Java: Spring Boot configurations that are entirely restructured between versions
• Ruby: AI generates Rails 6 code while your project uses Rails 7
• .NET: AI provides .NET 6 examples when you're building with .NET 8

The consequences are frustrating and time-consuming:

• Wasted debugging time fixing version incompatibilities
• Security vulnerabilities from outdated patterns
• Missing optimizations introduced in newer versions
• Incorrect error handling from mismatched API responses

The Package.json Problem

This version blindness is particularly problematic when AI assistants create project configuration files:

{
  "dependencies": {
    "next": "^13.0.0",
    "react": "^17.0.2"
  }
}

This creates a new project with significantly outdated versions, missing years of improvements and features. While the code will work with the examples the AI provides, the developer is starting with obsolete technology that might require extensive refactoring later.

Introducing ODocs: The Solution to Version Blindness

ODocs solves this problem through an open-source approach:

1. Documentation Registry: A central, open-source registry where framework maintainers across programming languages publish LLM-optimized documentation with specific version tags (e.g., nextjs:latest, nextjs:15.2, django:5.0). This includes version metadata and compatibility information to ensure AI-generated configuration files use appropriate versions.

2. Framework Detection: Tools that automatically scan your project files to identify which frameworks and versions you're using (package.json, requirements.txt, pom.xml, gemfile, etc.)

3. Vector Database: Local embedding of documentation for efficient retrieval, reducing token usage and enabling precise search capabilities.

4. AI Integration Layer: Open APIs that connect with any AI coding assistant via:

   • Model Context Protocol (MCP) integration
   • Tool-based integration for models like ChatGPT and Claude
   • Direct API integration for IDE providers

How ODocs Works in Your Workflow

For Existing Projects

When you use IDE extensions or run odocs serve in your project:

# Start the ODocs server (auto-detects frameworks)
$ odocs serve

→ Scanning package.json...
→ Detected frameworks:
  - nextjs:15.2.1
  - react:18.2.0
  - tailwindcss:3.3.0

→ Pulling documentation for detected frameworks...
→ Documentation pulled successfully

→ Embedding documentations for detected frameworks...
→ Documentation embedded successfully

→ API server running at http://localhost:2803/api
→ MCP server running at http://localhost:2803/mcp

The system:

1. Framework Detection: Automatically scans your project files to identify which frameworks and versions you're using.

2. Documentation Serving: Pulls the relevant documentation from the registry and embeds it in a local vector database for efficient retrieval.

3. AI Integration: Your AI assistant (ChatGPT, Claude, GitHub Copilot, etc.) connects to ODocs through MCP, API, or IDE plugins.

4. Contextual Code Generation: When you ask your AI assistant to generate code, it uses semantic search to find precisely relevant documentation, resulting in code that works with your specific version on the first try.

For New Projects

When creating new projects, the ODocs Documentation Registry provides AI assistants with accurate, real-time version information embedded within the documentation:

{
  "framework": "nextjs",
  "version": "15.2.1",
  "isLatest": true,
  "stableVersions": ["15.2.1", "15.0.0", "14.0.4", "13.5.6"],
  "compatibleWith": {
    "react": ">=18.0.0",
    "node": ">=18.17.0"
  },
  "releaseDate": "2024-09-15"
}

This version metadata is integrated directly within the documentation registry, ensuring AI assistants generate up-to-date package configurations and modern code patterns aligned with the latest stable versions.

See The Difference in Real-World Examples

Without ODocs: Version Mismatch

// AI suggests outdated dependency versions
// package.json created by AI
{
  "dependencies": {
    "next": "^13.0.0",
    "react": "^17.0.2"
  }
}

// Developer runs npm install and gets outdated Next.js 13 and React 17
// AI then generates code for older patterns, missing modern features
import { useState, useEffect } from 'react';
import { useRouter } from 'next/router';

export default function Product() {
  const router = useRouter();
  const { id } = router.query;
  const [product, setProduct] = useState(null);
  
  // Client-side fetching pattern from older Next.js
  useEffect(() => {
    if (id) {
      fetch(`/api/products/${id}`)
        .then(res => res.json())
        .then(data => setProduct(data));
    }
  }, [id]);

  if (!product) return <div>Loading...</div>;

  return (
    <div>
      <h1>{product.name}</h1>
      <p>${product.price}</p>
    </div>
  );
}

With ODocs: Version-Aware Code Generation

// AI suggests current dependency versions using ODocs version registry
// package.json created by AI with ODocs
{
  "dependencies": {
    "next": "^15.0.0",
    "react": "^18.2.0"
  }
}

// Developer runs npm install
// With version-specific knowledge, AI generates App Router code
import { notFound } from 'next/navigation';

// Server component with built-in data fetching
async function getProduct(id) {
  const res = await fetch(`https://api.example.com/products/${id}`);
  if (!res.ok) return undefined;
  return res.json();
}

export default async function ProductPage({ params }) {
  const product = await getProduct(params.id);
  
  // Modern error handling
  if (!product) {
    notFound();
  }

  return (
    <div>
      <h1>{product.name}</h1>
      <p>${product.price}</p>
    </div>
  );
}

Integration Options

ODocs integrates with your workflow through multiple methods, sorted by ease of adoption:

1. CLI Tools:

   • Zero-configuration command: odocs serve
   • Automatically detects framework versions from project files
   • Starts a local server with API and MCP endpoints
   • Your AI assistant connects to these endpoints for precise documentation retrieval

2. VS Code Extension:

   • One-click installation from VS Code Marketplace
   • Automatic detection of frameworks from project files
   • Runs a local MCP/API server in the background
   • Seamlessly connects to AI assistants through VS Code
   • Privacy-focused: your code never leaves your machine

3. GitHub Copilot Integration:

   • Companion functionality for GitHub Copilot users
   • Enhances Copilot suggestions with framework-specific context
   • Injects framework version information before code completion
   • Works automatically in the background

Current Development Status: Initial Proof of Concept Available

We're excited to announce that we've completed an initial working prototype of ODocs! While limited in scope, this proof of concept successfully demonstrates the core value proposition of solving version blindness in AI code generation.

Our current implementation focuses specifically on Hono 4.7.5, which already offers LLM-optimized documentation ready for AI consumption. This targeted prototype:

• Uses a documentation repository with version-specific Hono documentation
• Includes a CLI tool that successfully detects Hono in your projects
• Provides a local API server that connects with AI assistants
• Demonstrates how version-specific documentation improves code generation

We've also created a demo repository for odocs with openai with sample code that you can run to see the prototype in action. This simple demonstration shows how an AI assistant with access to version-specific documentation generates more accurate, compatible code compared to an assistant without this context.

While limited to a single framework version, this prototype validates our approach and provides a foundation for expanding to additional frameworks and versions in the coming months.

How You Can Get Involved

ODocs is an open-source project that thrives on community contributions. Get involved by:

For Framework Maintainers

We're especially looking for framework maintainers to help us expand beyond our initial Hono implementation. As a maintainer, you can:

• Contribute LLM-optimized documentation for your framework to our registry
• Help define standards for structuring documentation that works well with AI assistants
• Provide insights on version compatibility challenges specific to your ecosystem
• Integrate documentation publishing into your release workflows

For Developers

• Test our Hono prototype and provide feedback
• Contribute to the core ODocs infrastructure
• Help build IDE extensions and integrations
• Share your experiences with version mismatch issues

Your involvement is crucial to expanding ODocs to support all major frameworks across programming languages!

Join the Revolution!

We believe ODocs can significantly improve the developer experience with AI assistants by solving the version mismatch problem once and for all. But we can't do it alone!

Try our proof of concept! Check out our GitHub repository to test our Hono 4.7.5 implementation and see the difference version-aware AI code generation makes, even with this limited example.

This initial prototype sets the stage for our broader vision of supporting all major frameworks across programming languages. We're actively expanding beyond this first implementation and welcome contributors to help shape the future of version-aware AI coding.

Visit us at ODocs.dev to learn more, try the prototype with Hono 4.7.5, and contribute to this open-source initiative.

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Special thanks to Docker for inspiring our registry model approach, and to the Hono team for their LLM-optimized documentation that made our first prototype possible.

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ODocs: Bridging the version gap in AI code generation.