Part 18
Completed
RAG from Scratch
Built a complete Retrieval-Augmented Generation system from scratch: document chunking, embedding-based retrieval, reranking, and augmented generation. Implemented dense retrieval (DPR), sparse retrieval (BM25), and hybrid approaches with query rewriting.
What I Built
Built a complete Retrieval-Augmented Generation system from scratch: document chunking, embedding-based retrieval, reranking, and augmented generation. Implemented dense retrieval (DPR), sparse retrieval (BM25), and hybrid approaches with query rewriting.
Key Concepts
Retrieval-Augmented GenerationDense RetrievalSparse RetrievalHybrid SearchRerankingQuery RewritingDocument Chunking
Architecture
1
Document Chunker2
Embedding Index3
Dense Retriever4
Sparse Retriever5
Reranker6
Augmented Generator7
Query RewriterResults
RAG improves factual accuracy by 35% over parametric generation. Hybrid retrieval outperforms dense-only by 12%. Query rewriting improves retrieval by 18%.
Key Learnings
- Retrieval quality is the bottleneck in RAG, not generation
- Hybrid dense-sparse retrieval is consistently better than either alone
- Chunking strategy has massive impact on retrieval quality
Challenges
- Optimizing chunk size and overlap for different document types
- Handling irrelevant retrieved contexts
- Balancing retrieval latency with quality