In this article
Online evaluation monitors AI output quality continuously in production. Techniques include: LLM-as-judge (a secondary model scores each response on quality dimensions like faithfulness, relevance, and safety), user feedback collection (thumbs up/down, correction events), implicit engagement signals (dwell time, follow-up queries), and statistical sampling for human review. Tools like LangSmith Online Evals, Arize Phoenix, Braintrust, and PromptFoo support production eval infrastructure. Online evals close the feedback loop between deployment and quality improvement.
What it means in practice
Online Evaluation is not just vocabulary; it is a design handle. In AI observability, this term usually appears when engineers are designing, reviewing, or troubleshooting real production flows rather than only naming the concept. It becomes important after launch, when teams need evidence about quality, cost, regressions, and user-visible reliability.
Why engineers care
- It gives teams a shared name for the behaviour, risk, or architecture choice being discussed.
- It helps separate the goal from the implementation detail, so you can compare alternatives instead of copying a tool pattern blindly.
- It creates a useful checklist for reviews: inputs, outputs, failure modes, ownership, cost, latency, and measurement.
Production watch-outs
Avoid vanity metrics. Prefer measurements tied to user outcomes, regression prevention, incident response, and known quality risks.
Related context
Useful neighbouring concepts: LLM Evaluation, Offline Evaluation, Langsmith, Phoenix.