Models
Scoring an LLM application's behavior on live production traffic as it happens, rather than on a curated test set before deployment.
Scoring an LLM application's behavior on live production traffic as it happens, rather than on a curated test set before deployment. Because production traces have no reference outputs, online evaluators focus on reference-free checks such as safety, format, tone, and LLM-as-a-judge quality scores. Offline evaluation is the counterpart: it runs on datasets with expected answers and is used for pre-deployment and regression testing.
Primary source: docs.langchain.com
Example
Because production traces have no reference outputs, online evaluators focus on reference-free checks such as safety, format, tone, and LLM-as-a-judge quality scores.
Hands-on guides, comparisons, and tutorials that cover Models.
FAQ
Scoring an LLM application's behavior on live production traffic as it happens, rather than on a curated test set before deployment.
Online Evaluation sits in the Models part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Developers Digest publishes tutorials and videos that cover Models topics including Online Evaluation. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Scoring a model's output against an explicit, written rubric that defines what each score level means for each criterion, instead of asking for an open-ended quality rating.
A Claude feature where the model decides how much to reason based on task difficulty rather than a fixed thinking budget.
A Claude feature that gives the model a dedicated thinking phase before producing its visible response.

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