Case Study • RAG System
Smart Code and Documentation Search
How we improved code generation accuracy by 35% and accelerated documentation search for a product company
+35%
Generation Accuracy
300ms
Response Time
50K+
Files Indexed
10x
Faster Search
Client's Task
- •Developers spend up to 30% of their time searching for code and documentation
- •Standard repository search doesn't understand context and semantics
- •New employees take a long time to navigate the codebase
- •Code generation via ChatGPT doesn't account for company style and patterns
Project Goals:
- Create semantic search across the entire codebase
- Generate code in company style using existing patterns
- Speed up onboarding for new developers
- Answer code questions in natural language
Solution
RAG system for code with contextual understanding
Embeddings and LLM-based system that indexes code, understands its semantics, and generates answers considering the project context
Semantic search across 50K+ files in 300ms
Understanding dependencies between files and modules
Code generation in project style using existing patterns
Code questions answered in both Russian and English
IDE integration via plugin
Automatic index updates on repository changes
Results After 1 Month
| Metric | Before | After |
|---|---|---|
| Code generation accuracy | 60% | 95% |
| Information search time | 15 minutes | 30 seconds |
| New developer onboarding | 2 weeks | 3 days |
| System response time | — | 300ms |
| Developer satisfaction | — | 92% |
What the Client Gained
Developers save 2+ hours per day searching for information
Code generated considering company style and patterns
New hires become productive 5x faster
All documentation and code accessible through a single interface
System learns from every query and becomes more accurate
Technologies
OpenAI Embeddings • GPT-4 • Python • FastAPI • Qdrant • PostgreSQL • GitHub API