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10 essays on search, covering systems design, intelligence workflows, and the operational tradeoffs behind them.
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10 essays on search, covering systems design, intelligence workflows, and the operational tradeoffs behind them.
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Metadata Filtering in Vector Search: Pre-Filter, Post-Filter, and the Recall Cliff
How metadata filtering actually behaves in vector search: why post-filtering breaks tenant isolation, when selective filters collapse ANN recall, and what to do.
Embedding Model Selection for Retrieval: How to Choose Without Trusting a Leaderboard
How I pick an embedding model for retrieval: the constraints that decide it before quality does, a bake-off you can run, and the re-index nobody prices.
How to Evaluate RAG Retrieval: Eval Sets, Metrics, and Ship Gates
Retrieval eval is 20% metrics and 80% eval set. How I build one that survives re-indexing, which number to read at which k, and how to gate a change.
RAG Chunking Strategy: How to Split Documents So Retrieval Works
Chunking decides what your retriever can find. Here's how I split real documents — structure-first boundaries, parent-child units, and how to prove it worked.
Why Is My RAG Retrieval Bad? A Diagnostic Order of Operations
Bad RAG retrieval is four or five distinct failures wearing one costume. Here's how I localize which one you have before changing anything.
Reranking in RAG: How a Cross-Encoder Fixes Retrieval Quality
A cross-encoder reranker re-scores your top candidates by reading query and document together. Here's how I add one, size it, and prove it worked.
How to Build Hybrid Search with pgvector and BM25 in Postgres
Build hybrid search in Postgres with pgvector, tsvector, and RRF in one SQL query — the schema, index tuning, and when you actually need real BM25.
Hybrid Search vs Vector Search: Why RAG Retrieval Needs Both
Vector search understands meaning but fumbles exact identifiers; keyword search is the opposite. Here's how I build hybrid retrieval that does both — with fusion, reranking, and a pgvector setup.
Hybrid Search in Practice: Tuning Relevance Without Lying to Yourself
Relevance tuning is an operational discipline, not a one-time configuration. A deep dive into evaluation metrics, bias suppression, and feedback loops for intelligence systems.
The Hybrid Search Engine: Combining Lexical and Semantic Ranks
OSINT relevance is multi-modal. A technical exploration of why keywords fail and how to fuse BM25 with Vector Embeddings for operator-grade retrieval.