| name | rag-learning |
| description | Learn RAG by building real applications. Use when (1) building first RAG from scratch, (2) understanding RAG component design, (3) debugging retrieval problems, (4) optimizing retrieval quality, (5) comparing RAG frameworks. |
RAG Learning
Learning Path
Level 1: Minimal RAG
Build working RAG with LangChain + Chroma in 30 minutes.
For step-by-step: See references/level1-minimal.md
Level 2: Core Components
Understand and experiment with each RAG component.
For details: See references/level2-components.md
Level 3: Real Documents
Handle PDF, web pages, tables. Build personal knowledge base.
For details: See references/level3-documents.md
Level 4: Optimization
Implement Hybrid Search, Reranking, better prompts.
For details: See references/level4-optimization.md
Level 5: Production
Compare frameworks, choose vector DB, add caching.
For details: See references/level5-production.md
Quick Reference
Frameworks by Use Case
| Use Case | Framework | Why |
|---|
| Quick prototype | LangChain | Most examples, easy start |
| Data-heavy apps | LlamaIndex | Best data connectors |
| No prompting | DSPy | Programmatic LLM |
| Production RAG | RAGFlow | End-to-end solution |
Common Issues
| Problem | Solution |
|---|
| Chunk too large/small | Adjust chunk_size (500-1000 tokens) |
| Wrong embedding model | Try domain-specific model |
| Retrieved but wrong answer | Check prompt template |
| No results | Lower similarity threshold |
| Slow retrieval | Add caching, use hybrid search |
Resources
