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6 articles on Embeddings, written from production systems rather than documentation.
Benchmark rankings measure averages over corpora that are not yours. Four practical constraints narrow the field faster than any leaderboard position.
Chunk size is treated as a tuning parameter and behaves like an architectural decision. It sets the best answer your system can possibly retrieve.
Vector search handles paraphrase well and exact terms badly. Keyword search is the reverse, and users send both kinds of query in the same session.
Most RAG systems are built against a snapshot and then quietly decay. Indexing is a pipeline with a freshness target, not a one-off import script.
Retrieval optimises for recall and the model needs precision. A reranker sits between them and fixes the mismatch without touching the index.
Embeddings capture how text is used, not what it means. That distinction explains most of the retrieval failures that look inexplicable from the outside.
Whether it needs designing from scratch or rescuing from its own success, tell me what you’re building and I’ll tell you how I’d architect it.
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