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One stage of your pipeline now runs on a language model that writes plausible-sounding answers, and right now nobody on the team is checking whether those answers are actually true.
What you'll have at the end
A baseline hallucination rate for one LLM pipeline step, checked against retrieved sources, with a threshold that pages someone when it slips
You need
A pipeline step where a language model reads retrieved context and writes an answer, with both the retrieved context and the generated answer logged somewhere you can pull them back out later.
Not covered
Tuning what the retrieval side of the pipeline fetches or how it ranks results. This recipe starts from whatever context that stage already hands the model and only checks whether the written answer stays inside it.
Your loudest data quality alert stopped meaning anything
once this new alert has been live a while and starts feeling too noisy or too quiet, that recipe re-derives its floor properly instead of you nudging the number by feel
Write your first data SLA
once you want the faithfulness floor written down as a commitment the rest of the team can see, instead of a number sitting only in your alerting config
Checked 26 Aug 2026
Part of the Data Observability cookbook