A practical guide to designing RAG systems. Covers document ingestion, chunking, embeddings, vector search, reranking, prompt composition, and evaluation from a real product engineering perspective.
Chunking and embeddings define the floor of retrieval quality. This post covers chunk size, overlap, heading preservation, code block handling, embedding model selection, and indexing strategy.
Search quality largely defines RAG quality. This post explains dense retrieval, BM25, hybrid search, query rewriting, metadata filtering, and reranking from a practical engineering perspective.
A practical guide to building the RAG data layer for an AI stock investment Agent. Covers price data, news, SEC filings, earnings transcripts, normalization, chunking, metadata, and freshness-aware retrieval.