Hybrid retrieval: fuse keyword and vector search
Vector search alone misses exact identifiers and rare terms. Add Postgres full-text search, fuse the rankings with RRF, and rerank the top slice.
Chunking that survives contact with real documents
Build the ingestion half of a retrieval pipeline: structure-aware splitting, contextual headers, and a schema that lets you re-chunk without re-embedding the world.
The cache you think you have
Your hit rate is 4% and you can't see why. A field guide to the invalidations nobody writes down — in HTTP caches, in Postgres, and in LLM prompt prefixes.
Full-text search in Postgres, without Elasticsearch
Weighted tsvectors, generated columns, ranked results, highlighted snippets, and typo tolerance — a complete search feature in one table.
Postgres is probably enough
Before you add Redis, Elasticsearch, a vector database, and a queue: Postgres does all four adequately, and 'adequately' is the correct target until it isn't.