Advanced RAG Patterns — From Naive RAG to Production Quality

Naive RAG isn’t enough. Sometimes it returns irrelevant context, sometimes it hallucinates. For production, you need advanced techniques.

Problems with Naive RAG¶

• Semantic gap: The query and document may not be semantically similar
• Lost in the middle: LLMs ignore context in the middle
• Multi-hop queries: Require chaining

Query Transformation¶

Query expansion: 3–5 query variants. Query decomposition: breaking complex queries into sub-queries.

Hybrid Search + Reranking¶

Vector + BM25 (Reciprocal Rank Fusion). Cross-encoder reranking: retrieve top-50, rerank to top-5.

Chunking Strategies¶

• Semantic chunking: Boundaries based on semantic shifts
• Parent-child chunks: Retrieve child, context from parent
• Metadata enrichment: Source, date, category

RAG Is a Spectrum, Not a Binary State¶

Invest in evaluation (RAGAS) — without metrics, you won’t know what to improve.