12 posts · page 1 of 1
Learn fixed-size, semantic, recursive, and document-aware chunking strategies for RAG pipelines. Includes overlap techniques, benchmarks, and Python code.
Learn how to combine BM25 keyword search with vector similarity using reciprocal rank fusion for better RAG retrieval. Includes Python code and benchmarks.
Improve RAG retrieval quality with advanced techniques: query expansion, multi-query retrieval, cross-encoder reranking, and reciprocal rank fusion.
Build a production-ready RAG pipeline with document ingestion, chunking, retrieval, and generation. Covers error handling, monitoring, and optimization.
A practical introduction to vector search with FAISS: how indexes work, which index to pick, and how to wire it into a real retrieval pipeline for embeddings.
RAG and grounding are often used interchangeably but they describe different techniques. Here is how to tell them apart and when each one matters.
Learn how chunk overlap rescues boundary context in RAG pipelines, with practical strategies for choosing overlap size and shape for different corpora.
Combine lexical BM25 with dense vector search to recover the queries each method misses on its own and boost RAG retrieval quality.
Learn how Hypothetical Document Embeddings (HyDE) improve RAG recall by embedding a generated answer instead of the raw query, with examples and trade-offs.
How to use metadata filters in RAG to improve precision, scope retrieval, and enforce permissions without sacrificing recall.
Add a reranker on top of vector retrieval to dramatically improve top-k quality with cross-encoders, late interaction, and LLM rerankers.
Practical retrieval strategies for RAG: chunking, hybrid search, reranking, query rewriting, metadata filtering, and evaluation patterns that actually move the needle.