9 articles tagged with Eval.
A demo RAG app is easy; one users trust is not. This is the map for reliable retrieval-augmented generation: grounding, evaluation, retrieval quality, guardrails, and safe rollout.
A prompt tweak or model bump can quietly wreck answers everywhere. Ship LLM changes the way you ship risky code: gate, shadow, canary, roll back.
A RAG system that invents facts erodes trust fast. Here is how we ground answers, force citations, and catch the fabrications before users do.
A user got our support bot to recite its system prompt and then draft a refund it wasn't authorized to give. Two layers of guardrails, one on input, one on output, closed both holes.
A prompt tweak that helped one case quietly broke twenty others. Here's the CI eval harness we built so that never ships silently again.
You can't improve retrieval you don't measure. The offline eval harness that lets us change embeddings, chunking, and rerankers with confidence instead of vibes — with the metrics that actually predict production quality.
Parsing model output with a regex and a prayer doesn't survive contact with traffic. The validation layers that keep structured LLM output reliable — constrained decoding, schema validation, and the repair loop.
Most LLM eval suites correlate poorly with what real users experience. The eval patterns we run that move with prod metrics — and the ones that lied to us.
A single quality score multiplies two failure modes together and hands you a number you can't factor back apart. Split it, and a regression tells you which half of the pipeline broke.
