#RAG
8 pieces filed under “RAG”.
Citation design: showing the AI's working
Citations are how AI answers earn belief. Inline sources, confidence without fake precision, expandable evidence, link rot, and how provenance UI measurably builds trust.
AI answers vs good navigation: choose deliberately
Replacing your site's navigation with an AI answer box is rarely the upgrade it looks like. A decision framework for AI search vs structured browse, honestly measured.
RAG and the staleness problem: freshness is a ranking signal
Stale retrieval makes RAG answers confident folklore. Treating freshness as a ranking signal, surfacing 'last updated' in answers, and designing the re-indexing pipeline.
Citation design: making RAG answers checkable
Citations only work if they're checkable: claim-to-chunk mapping, snippet fidelity tests, anchor UX, and what to do when the source contradicts the summary.
Chunking is a design decision: RAG retrieval craft
Chunk sizing, structure-aware splitting, metadata, hybrid retrieval and reranking — the retrieval engineering that decides whether your RAG feature answers or shrugs.
Fine-tuning vs RAG vs prompt: a decision guide
Prompt, retrieve or fine-tune? The decision tree we run with clients — real cost curves, data needs, iteration speed and maintenance burden.
RAG pitfalls we hit so you don't have to
Retrieval in production humbles everyone. The chunking trade-offs, citation bugs, stale knowledge and eval failures that taught us how RAG actually breaks.
Pylon Care: an AI support assistant that knows its limits
Pylon Health's support queue drowned in the same five questions. We shipped an AI assistant with citations, red lines and a kill switch — evals first.