RAG Part 2: Optimization Techniques

Advanced strategies to improve RAG retrieval quality, reduce latency, and handle complex queries

RAG Vector Search Embeddings Optimization

RAG Part 2: Optimization Techniques

Building a basic RAG pipeline is easy, but making it production-ready requires optimization. In this post, we’ll explore techniques to enhance retrieval quality and performance.

1. Chunking Strategies

How you split your text matters.

  • Fixed-size Chunking: Simple but may break context.
  • Semantic Chunking: Splits text based on meaning/topics.
  • Recursive Chunking: Tries to keep related text together using separators.

Don’t rely on vector search alone.

  • Keyword Search (BM25): Good for exact matches (names, IDs).
  • Vector Search: Good for semantic meaning.
  • Hybrid: Combine both with a re-ranking step for best results.

3. Re-ranking

Retrieving the top-k documents isn’t enough. Use a Cross-Encoder model to re-score and re-order the retrieved chunks to ensure the most relevant ones are passed to the LLM.

4. Metadata Filtering

Use metadata (dates, categories, authors) to filter search results before vector search. This improves speed and relevance.

5. Query Transformation

  • Query Expansion: Generate synonyms or related questions.
  • HyDE (Hypothetical Document Embeddings): Generate a fake answer and search for documents similar to that answer.

Next Steps

In Part 3, we will look at Advanced Implementation and how to build a complete RAG system with code examples.