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Contextual Retrieval

4 articles · Page 1 of 1
Su San Talks Tech
Su San Talks Tech
Oct 14, 2026 · Artificial Intelligence

RAG Deep Dive: Principles, Optimization & Production Implementation

This comprehensive guide explains RAG from first principles through production deployment, covering offline indexing (loading, chunking, embedding, vector storage), online query pipeline (retrieval, reranking, prompt assembly), hybrid search with BM25 and dense vectors, RRF fusion, cross-encoder reranking, evaluation metrics (recall@k, MRR, nDCG), query rewriting, metadata filtering, contextual retrieval, Agentic RAG, and a real-world case study building a style-matching writing assistant with 197 articles.

Agentic RAGChunking StrategiesContextual Retrieval
0 likes · 43 min read
RAG Deep Dive: Principles, Optimization & Production Implementation
AgentGuide
AgentGuide
Jun 11, 2026 · Artificial Intelligence

RAG Chunking Strategies and How to Balance Chunk Size (Full Answer)

The article outlines four common RAG chunking methods—fixed-length, recursive, semantic, and structure-based—explains their pros and cons, discusses the trade‑off between chunk size, retrieval precision and context completeness, and introduces two advanced techniques, Contextual Retrieval and Late Chunking, with empirical results from Anthropic and Jina AI.

ChunkingContextual RetrievalFixed‑Length Splitting
0 likes · 8 min read
RAG Chunking Strategies and How to Balance Chunk Size (Full Answer)
JavaEdge
JavaEdge
Oct 2, 2024 · Artificial Intelligence

Boost RAG Retrieval Accuracy with Contextual Embeddings and BM25

This article presents a contextual retrieval technique that combines contextual embeddings and contextual BM25 to reduce RAG miss rates by up to 67%, explains the underlying methods, implementation steps, cost considerations, experimental results, and practical deployment guidance.

AIBM25Contextual Retrieval
0 likes · 17 min read
Boost RAG Retrieval Accuracy with Contextual Embeddings and BM25