OpenAI Evaluation: Test Prompts Before You Rely on Them

OpenAI evaluation means testing model outputs against defined requirements using examples that resemble your actual work. Start with a repeatable task, save its current results, change one part of the workflow, and compare both versions against the same criteria.

The test runner produces evidence; someone still needs to explain why a change was accepted. Tools like fenbs keep prompt instructions, evaluation decisions, and lessons outside one chat so another assistant can continue the work.

How do you run a useful first evaluation?

1. Choose one outcome, such as turning a project update into a draft client email. Write what a usable result must contain and what would make it unsafe to use.
2. Collect a small, varied set of inputs you are allowed to use. Include incomplete information and conflicting notes alongside ordinary examples.
3. Run the current prompt on every case and save the exact inputs and outputs. This is your baseline: the result a proposed improvement must beat.
4. Change one instruction or context source, rerun the same cases, and review the differences. Record failures by cause before deciding what to keep.

OpenAI distinguishes evaluation as a practice from its Evals product, which is being retired. Check the official transition notice↗ (opens in a new tab) before following dashboard instructions. The workflow below applies whether you review outputs manually or use a maintained evaluation runner.

For existing Evals users, OpenAI provides a migration guide to Promptfoo↗ (opens in a new tab). Keep test inputs, expected behavior, and scoring rules readable outside the interface you started with. A tool change should not erase the meaning of your tests.

What exactly should count as a passing answer?

A pass condition describes something a reviewer can observe. For a client update, require the approved completion date, the known delay, and the next action. Reject any answer that invents approval, changes a supplied number, or turns an unresolved question into a promise.

Separate mandatory checks from preferences. A pleasant tone cannot compensate for a wrong deadline. You might accept two different introductions while requiring both to preserve the same facts. Write that distinction down before comparing outputs, because an attractive rewrite can otherwise move the goalposts.

Agree on these requirements with the person responsible for the work. Save their approval in fenbs Decisions and rules so a later reviewer knows which criteria were actually accepted.

Which cases reveal problems that a demo misses?

Use cases that differ in the ways your real requests differ. A short, tidy update tests basic writing. A messy update with two possible dates tests how the workflow handles uncertainty. A note containing a client suggestion and a separate approved decision tests whether it can tell those apart.

• An ordinary case with all required facts present.
• A missing-source case where the correct response asks for information.
• A contradiction case where newer approved material replaces an older note.
• A boundary case containing text that asks the assistant to ignore the task rules.
• A long or awkwardly formatted case that resembles what people actually submit.

Label invented examples as hypothetical. If you use real failures, remove information you are not authorized to share and preserve the feature that caused the failure. Replacing a confusing approval chain with a perfectly clear paragraph can accidentally remove the very problem the case should test.

Keep some cases separate while refining the prompt. Then use those untouched cases to check whether the revision helps beyond the examples you just studied. A prompt that repeats the answer to familiar cases may still fail when the next request uses different language.

Can you trust an AI to grade another AI?

An automated grade is another result to validate. Start with human reviews of both passing and failing outputs, especially when a check depends on meaning. Compare the automated judgment with the reviewer’s reasons. If they disagree, investigate the grading rule before treating a higher score as progress.

Use simple checks where they match the requirement. A required heading can be checked directly; a faithful summary needs a judgment about the source. OpenAI’s evaluation guidance↗ (opens in a new tab) recommends task-specific tests and calibrating automated scoring with human feedback.

Review individual failures as well as the overall result. Imagine a revision improves tone across most emails but invents a deadline in one difficult case. That failure can outweigh the stylistic improvement. Keep serious failures visible instead of burying them inside an average.

How do you compare changes fairly?

Name each prompt revision and record the model, source material, available tools, and test date. Hold these constant when you are testing one instruction change. If you change the model and replace the files simultaneously, a better answer will not tell you which change helped.

Where outputs vary, repeat the important cases. Save the range of behavior, not just the best answer. Also review time spent correcting the result: a longer draft that needs no factual repair may be more useful than a shorter draft requiring careful reconstruction.

Write a short conclusion that identifies the accepted revision, remaining failures, and next experiment. The broader AI prompts library covers instruction design; evaluation supplies evidence that a particular change improved your particular task.

How to manage evaluation work with fenbs

1. Create a free account and add a project for the workflow you are testing.
2. Add a task describing the evaluation objective. Put the cases to review, evidence location, and acceptance checks in its plan.
3. Save the reusable instruction in Skills and prompts. Name revisions in the text so the task can identify the exact version tested.
4. Record the person’s acceptance decision in Decisions and rules, then add a lesson for a failure that should change future work.
5. Before stopping, save Where we left off with the accepted version, unresolved failures, and the next comparison to run.

fenbs does not execute evaluations or grade model outputs. Keep runnable test data and detailed results in the appropriate testing tool; use fenbs for the work and reasoning around them. It is a poor fit if your only need is a test runner.

For more on testing whole assistant systems, see AI assistant evaluation; the agentic workflows guide explains the surrounding work. Keep the evaluation conclusion available to whoever maintains that workflow next.

Task-specific examples belong in the guides to Excel, n8n, code review, and translation; use each task’s real requirements when choosing cases.