Retrieval Metrics Demystified: From BM25 Baselines to EM@5 & Answer F1
What BM25, Recall@k, answer-in-context rate and token F1 actually measure, which number to fix first, and how to compute them without fooling yourself.
Retrieval and RAG01
Series · 8 parts · 2024–2026
Which retrieval numbers actually matter, a reproducible way to find out which stack to build on, and then the three ways to give a model structure it can navigate: a graph built by extraction, a tree borrowed from the document itself, and knowledge written down by hand in a format anything can read.
What BM25, Recall@k, answer-in-context rate and token F1 actually measure, which number to fix first, and how to compute them without fooling yourself.
Retrieval and RAG01
GraphRAG turns a corpus into entities, communities and LLM-written reports so it can answer whole-corpus questions that top-k retrieval structurally cannot.
Retrieval and RAG02
How to benchmark sparse, dense, hybrid and late-interaction retrieval on your own corpus, and how to read the numbers without fooling yourself.
Retrieval and RAG03
PageIndex indexes a document as its own table of contents and lets a model walk the tree. No embeddings, no chunking, and a route you can show a reader.
Retrieval and RAG04
OKF is a folder of markdown files that tells an AI agent what your data means, who wrote it, who checked it, and when it stops being true.
Retrieval and RAG05
How to run keyword, dense, fusion and reranking together on modest hardware: where the memory actually goes, and which famous constants are folklore.
Retrieval and RAG06
Every retrieval method assumes clean structured text. Getting it out of a PDF is its own problem, and Docling is the 2026 answer worth knowing.
Retrieval and RAG07
Five file conventions claim to be the standard for AI agents. Here is what each one answers, who really reads it, and the evidence behind both claims.
Retrieval and RAG08