Why RAG Matters

Retrieval-Augmented Generation (RAG) is the most practical way to ground LLM responses in your own data. But building a production-ready system requires more than just connecting a vector database.

Chunking Strategies

  • Fixed-size: Simple but may split semantic units.
  • Semantic: Split by meaning using embedding similarity.
  • Recursive: Split by headers, paragraphs, then sentences.
  • Document-aware: Respect Markdown/HTML structure.

Hybrid Search

Combine dense vector search with sparse keyword search (BM25). This catches both semantic matches and exact term matches.

Re-ranking

After initial retrieval, use a cross-encoder model to re-score results. This significantly improves precision.

Evaluation

  • Faithfulness: Does the answer stick to retrieved context?
  • Relevance: Are the retrieved chunks actually useful?
  • Answer correctness: Is the final answer factually correct?

Tools & Frameworks

  • LlamaIndex: High-level RAG framework with built-in recipes.
  • LangChain: Flexible orchestration layer.
  • RAGAS: Purpose-built evaluation framework.
  • Chroma / Qdrant / Weaviate: Vector databases with different trade-offs.

Advanced Chunking Strategies

Default chunking (split by character count) often breaks semantic meaning. Better approaches:

  • Semantic chunking: Use an LLM to identify topic boundaries, then split at those points.
  • Recursive splitting: Split by heading > paragraph > sentence. Preserve hierarchy.
  • Overlap: Add 10–20% overlap between chunks to preserve context at boundaries.
  • Metadata enrichment: Tag each chunk with source, date, author, and category for filtered retrieval.

Evaluation Framework

Measure RAG quality with these metrics:

  • Retrieval precision: Are the right chunks being fetched? Test with 50+ query-chunk pairs.
  • Faithfulness: Does the answer only use information from retrieved chunks? Use LLM-as-judge.
  • Answer relevance: Is the answer actually addressing the question?
  • Latency: End-to-end time from query to response. Target <3 seconds.

Production Checklist

  • [ ] Implement hybrid search (BM25 + vector similarity).
  • [ ] Add re-ranking step (Cohere Rerank or cross-encoder).
  • [ ] Set up monitoring for retrieval quality and drift.
  • [ ] Create a feedback loop: track which answers users mark as helpful.
  • [ ] Version your knowledge base and embeddings separately.

Advanced Chunking Strategies

Default chunking (split by character count) often breaks semantic meaning. Better approaches:

  • Semantic chunking: Use an LLM to identify topic boundaries, then split at those points.
  • Recursive splitting: Split by heading > paragraph > sentence. Preserve hierarchy.
  • Overlap: Add 10–20% overlap between chunks to preserve context at boundaries.
  • Metadata enrichment: Tag each chunk with source, date, author, and category for filtered retrieval.

Evaluation Framework

Measure RAG quality with these metrics:

  • Retrieval precision: Are the right chunks being fetched? Test with 50+ query-chunk pairs.
  • Faithfulness: Does the answer only use information from retrieved chunks? Use LLM-as-judge.
  • Answer relevance: Is the answer actually addressing the question?
  • Latency: End-to-end time from query to response. Target <3 seconds.

Production Checklist

  • [ ] Implement hybrid search (BM25 + vector similarity).
  • [ ] Add re-ranking step (Cohere Rerank or cross-encoder).
  • [ ] Set up monitoring for retrieval quality and drift.
  • [ ] Create a feedback loop: track which answers users mark as helpful.
  • [ ] Version your knowledge base and embeddings separately.