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.
Build working RAG with LangChain + Chroma in 30 minutes.
For step-by-step: See references/level1-minimal.md
Understand and experiment with each RAG component.
For details: See references/level2-components.md
Handle PDF, web pages, tables. Build personal knowledge base.
For details: See references/level3-documents.md
Implement Hybrid Search, Reranking, better prompts.
For details: See references/level4-optimization.md
Compare frameworks, choose vector DB, add caching.
For details: See references/level5-production.md
| 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 |
| 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 |