Hybrid retrieval in practice: what keyword search finds that embeddings miss, how to fuse two ranked lists, when a reranker earns its latency, and how to tell which signal failed.https://context-augmented-llm.com/https://context-augmented-llm.com/blogs/which-signal-failed/https://context-augmented-llm.com/blogs/which-signal-failed/A query returned nothing useful. A procedure for finding whether lexical missed, dense missed, fusion buried it, or the document was never indexed.Sun, 26 Jul 2026 00:00:00 GMTdiagnosisfusionhttps://context-augmented-llm.com/blogs/when-a-reranker-earns-its-latency/https://context-augmented-llm.com/blogs/when-a-reranker-earns-its-latency/A cross-encoder reads the query and passage together, which is why it ranks better and costs more. Where it belongs, what to feed it, and when to skip it.Sat, 25 Jul 2026 00:00:00 GMTrerankingtradeoffshttps://context-augmented-llm.com/blogs/fusing-two-ranked-lists/https://context-augmented-llm.com/blogs/fusing-two-ranked-lists/BM25 scores and cosine similarities aren't on the same scale. Reciprocal rank fusion, score normalisation, and what each method throws away.Fri, 24 Jul 2026 00:00:00 GMTfusiontradeoffshttps://context-augmented-llm.com/blogs/what-keyword-search-misses/https://context-augmented-llm.com/blogs/what-keyword-search-misses/Vocabulary mismatch: when the user's words and the document's words don't overlap, term scoring has nothing to score. Where stemming stops helping.Thu, 23 Jul 2026 00:00:00 GMTlexicaltradeoffshttps://context-augmented-llm.com/blogs/what-vector-search-misses/https://context-augmented-llm.com/blogs/what-vector-search-misses/Identifiers, rare terms, negation and exact phrases. Five query classes where embeddings return plausible neighbours instead of the document you asked for.Wed, 22 Jul 2026 00:00:00 GMTdensetradeoffs