OpenAI Evals for prompt testing

OpenAI Evals is a framework for testing and measuring prompt/model behavior in a repeatable way. It’s useful when you want to compare prompts, agents, or model configurations against the same set of tasks and score them consistently.

What it’s for

• Prompt regression testing: check whether a prompt change improved or broke behavior
• Model comparisons: compare GPT versions or settings
• Task-specific evaluation: accuracy, format compliance, safety, style, etc.
• CI/CD integration: run evals automatically before shipping prompt changes

Typical workflow

1. Define the task
   • Example: “Extract invoice total from text”
2. Create test cases
   • Inputs, expected outputs, edge cases
3. Choose a scorer
   • Exact match, fuzzy match, model-graded, rubric-based, etc.
4. Run evals
   • Feed the same inputs through your prompt/model
5. Inspect results
   • Overall score, per-case failures, common patterns
6. Iterate
   • Tune prompt and rerun

Common eval types

• Classification / exact match
• Structured output validation (JSON schema, regex, parsing)
• LLM-as-judge scoring
• Pairwise comparison between outputs
• Human review for subjective tasks

Example use case

If you have a prompt that extracts shipping addresses, you can:

• build 50 example inputs
• define the expected JSON fields
• score whether the output is valid and complete
• compare Prompt A vs Prompt B

Where OpenAI Evals fits

OpenAI Evals is especially helpful when prompts are:

• sensitive to wording
• used in production
• required to follow strict formats
• hard to judge manually at scale

Good practices

• Include easy, hard, and adversarial cases
• Keep a frozen test set for regression testing
• Measure both task success and format correctness
• Use multiple metrics if one metric is too narrow

If you want, I can also give you:

1. a simple OpenAI Evals setup example,
2. a JSON/YAML eval config template, or
3. guidance on how to test prompts for GPT-based apps.