Users often saw the assistant disclaim ("Sorry, I couldn't find specific
context regarding X, but here's a general answer...") even when material
that directly answered the query was embedded. Two compounding causes,
both on the read/generation side:
1. The rag_context system prompt explicitly authorized the hedge: it told
the model to fall back to general knowledge and "acknowledge the
limitations," and mandated "According to the information available..."
citations that pushed small models into meta-commentary preambles.
Rewrote it to treat the retrieved context as the primary, authoritative
source, lead with the answer, fall back to general knowledge *silently*,
and never emit "couldn't find specific context" phrasing, while still allowing
the model itself to make some "sanity-check" decisions if the retrieved context
seems wildly incorrect or unrelated to the user's query.
2. The injected context labeled each block with a raw relevance score
(e.g. "Relevance: 42.3%"). nomic-embed cosine scores for genuinely
relevant passages sit ~0.4-0.6, so the number primed the model to
distrust correct context. Dropped the model-visible score (it stays in
the logs) and replaced it with a neutral source-title label.
Also added a conservative, score-scaled heading boost in rerankResults:
when query keywords match a chunk's section/article title (ZIM metadata we
already fetch), nudge its rank. Same diminishing-returns shape as the
existing boosts and gated behind the existing semantic-quality threshold,
so it can't promote a weak match.
Scope note: this removes the visible hedge and improves ranking of
already-retrieved chunks. It does NOT fix retrieval recall — diluted
1500-token chunks that never reach dense search's top-k are unaffected.
That's a follow-up (smaller chunks + BM25/RRF hybrid).
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