Case 01
AI Developer Copilot
RAG, agents, and MCP over a living codebase
Overview
An AI-powered developer assistant that connects to a GitHub repository, understands the codebase, and answers questions about architecture, implementation, defects, and specific modules.
Problem
Large codebases are difficult to understand and navigate by hand. Developers lose time searching files, tracing dependencies, and locating the implementation that actually matters.
Architecture
Architecture · 01
- 01
Repository ingestion
- 02
Code-aware chunking
- 03
Embeddings
- 04
Semantic retrieval
- 05
Agent + MCP tools
- 06
Grounded response
A repository ingestion pipeline processes source code, forms meaningful chunks, generates embeddings, and stores them for semantic retrieval. A RAG layer retrieves relevant code before generation. An agent layer can call tools such as repository search and GitHub APIs, with MCP exposing those tools in a standard interface.
Engineering
The system is designed around retrieval quality rather than prompt theatrics: chunking has to respect code structure, context has to survive file boundaries, and tool calls have to stay disciplined when a repository is large.
Challenges
- Code-aware chunking that preserves meaning
- Retrieval quality across large repositories
- Maintaining context across files and modules
- Tool calling and agent orchestration
- Suppressing irrelevant retrieval