OpenAI Evals Using Phoenix
OpenAI Evals are used for evaluating LLM models and finding accuracy, which helps compare custom models and figure out how well your model performs.
Apr 2, 2024 · 1 min read · Som
You can't improve what you don't measure. Evals give an LLM app a repeatable score, and Arize Phoenix adds the tracing and visualization that make those scores actionable.
Why evals matter
Prompt tweaks and model swaps feel like improvements — until a regression slips through. An eval suite turns "seems better" into a number you can track across versions.
The loop
- Define a dataset of inputs with expected outcomes.
- Run your model/prompt to produce outputs.
- Grade with exact-match, heuristics, or an LLM-as-judge.
- Inspect traces and failures in Phoenix.
Getting value
Start small: a few dozen representative cases catch most regressions. Grow the suite from real production failures, and gate releases on the score.