Category archive
Retrieval is an evidence pipeline, not a similarity score.
Most retrieval failures are read as model failures. These articles stay on the retrieval side of that line: how candidates are generated, how they are ranked before generation sees them, how an index drifts away from the corpus it was built from, and how the evidence a model is handed decides what it can honestly say.
Articles in Retrieval
Production RAG Is an Evidence Pipeline, Not a Vector Search
Designing retrieval-augmented generation around ingestion quality, query planning, evidence assembly, and verifiable answers.
Hybrid Lexical and Vector Search
A practical design for candidate generation, score normalization, rank fusion, metadata filters, and retrieval evaluation.
Rerankers: The Quality Layer Between Search and Generation
How cross-encoders, late interaction, listwise ranking, and calibration turn noisy candidates into usable evidence.
Embedding Systems: Models, Indexes, and Semantic Drift
Selecting representations, constructing embedding inputs, migrating indexes, and detecting when semantic neighborhoods stop serving the product.
AI Data Flywheels Without Feedback Pollution
Turning product use into better data through instrumentation, review, provenance, counterfactuals, and controlled training loops.
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Put this against a real system.
If a decision in Retrieval is in front of you right now, the fastest version of this is a call: bring the architecture, the failure you are seeing, and the constraint you cannot move.
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