AI Vector Search with FAISS
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.
·4 min read · #ai#vector-search#faiss
#retrieval
8 posts · page 1 of 1
Grounding vs RAG: What's the Actual Difference?
RAG and grounding are often used interchangeably but they describe different techniques. Here is how to tell them apart and when each one matters.
·4 min read · #llm#rag#grounding
RAG Chunk Overlap Strategies
Learn how chunk overlap rescues boundary context in RAG pipelines, with practical strategies for choosing overlap size and shape for different corpora.
·4 min read · #rag#chunking#overlap
RAG Hybrid Search: BM25 + Vectors
Combine lexical BM25 with dense vector search to recover the queries each method misses on its own and boost RAG retrieval quality.
·4 min read · #rag#bm25#hybrid-search
RAG HyDE: Hypothetical Document Embeddings
Learn how Hypothetical Document Embeddings (HyDE) improve RAG recall by embedding a generated answer instead of the raw query, with examples and trade-offs.
·4 min read · #rag#hyde#embeddings
RAG Metadata Filtering Strategies
How to use metadata filters in RAG to improve precision, scope retrieval, and enforce permissions without sacrificing recall.
·4 min read · #rag#metadata#retrieval
RAG Reranking Models Overview
Add a reranker on top of vector retrieval to dramatically improve top-k quality with cross-encoders, late interaction, and LLM rerankers.
·4 min read · #rag#reranker#cross-encoder
RAG Retrieval Strategies
Practical retrieval strategies for RAG: chunking, hybrid search, reranking, query rewriting, metadata filtering, and evaluation patterns that actually move the needle.
·5 min read · #ai#rag#retrieval