OpenAI Evals: A Complete Guide

What Are OpenAI Evals?

OpenAI Evals is a simple framework for testing how well language models perform on specific tasks. Think of it as a toolkit that helps teams turn expectations into repeatable checks. Instead of relying on gut feel, you define datasets, scoring rules, and runner scripts so you can measure whether a model behaves the way you expect. The aim is clarity and repeatability: run the same tests today and tomorrow and compare the results.

Definition of an Eval

An eval is a single, executable test. It ties together a set of inputs, the model you want to call, and the logic that judges the response. You can configure evals with runtime settings like temperature or sampling, and run them locally, in CI, or on a schedule. The output is a set of scored examples and simple metrics that show where the model passes and where it needs work.

How Evals Work

The flow is straightforward. Start with a dataset of prompts that represent how users will interact with your system. Run those prompts against a model and collect the responses. Score each output using rule-based checks or a grader model. Finally, aggregate the results into pass rates, error patterns, and per-example traces so you can see what went wrong and why.

Why Use Evals?

Evals give teams a reliable way to measure progress. They make subjective judgements objective. Instead of debating whether a model is better, you can point to scores and example-level diffs. Evals also help catch regressions before they reach production and provide a shared language between engineers, product managers, and QA for what success looks like.

Benefits for LLM-based Applications

Evals help with model selection, debugging, and monitoring. They let you isolate failure modes, compare models on exact same inputs, and track drift over time. When you tie eval outcomes to business goals, they reduce guesswork and speed up iteration.

How Evals Fit into Your Development Workflow

Use small, targeted evals during rapid prototyping. Broaden the suite when tuning models. Add automated eval runs to CI so critical failures block a release. Keep a small smoke set for quick checks and a larger nightly suite for broader coverage. The point is to bake evaluation into development, not treat it as an afterthought.

Types of Eval Templates

Basic Eval Templates

Match Template

This is the strict check. The model output must exactly match what you expect. Use this for deterministic tasks where wording and format matter, such as structured JSON responses or fixed labels. It’s blunt but very effective for catching formatting regressions.

Includes Template

Instead of exact matching, this check looks for required tokens or phrases. It’s useful when the response can be written in many ways but must contain certain elements. For example, you might check that a generated policy includes the phrase consent required or that a product description mentions key attributes.

Fuzzy Match Template

Use fuzzy matching when valid answers vary in wording. This approach scores similarity rather than equality. Typical methods include token overlap or edit distance. Fuzzy checks let you accept natural paraphrases while still catching outputs that drift too far from the intended answer.

Model-Graded Eval Templates

Fact-checking

Fact-checking templates defer judgment to a grader model or a verification step. Instead of relying on brittle rules, you ask a trusted grader to compare the model output against sources or known facts. This is useful for claims that are tricky to validate with simple rules.

Closed Domain Q&A

Use this for question and answer tasks where the answer set is known. The evaluator verifies whether the returned answer is an allowed item. It keeps scoring precise in domains like product specs or policy lookups.

Naughty Strings Security Eval

This template feeds adversarial or malformed inputs to the model to surface unsafe behaviors, injections, or content policy issues. Treat it like a safety check that runs alongside functional tests.

Implementing OpenAI Evals in Your Project

Creating the Dataset

Datasets are typically JSONL or YAML files with input, expected output, and any metadata you need. Keep examples focused, labeled, and realistic. Include edge cases and failure examples. Store datasets in a versioned location so the team can reuse, review, and update them.

Example

{"input": "Translate to French: Hello", "expected": "Bonjour"}

{"input": "What is 2+2?", "expected": "4"}

Aim for examples that mirror real usage rather than contrived tests.

Creating a Custom Eval

Custom evals are small, testable Python classes that implement your scoring logic. Keep them simple and modular so you can reuse them across different datasets. The class handles a single example and returns a score or pass/fail result. Wire this class into a YAML config that declares the dataset and runtime parameters.

Example sketch

from evals import Eval

class MyCustomEval(Eval):

    def run_example(self, example, model_output):

        if "expected phrase" in model_output:

            return {"score": 1}

        return {"score": 0}

Running the Eval

Run evals from the command line or scripts. A typical invocation might look like

oaieval my_eval --model gpt-4

Pin model versions and seeds for reproducibility when randomness is involved. Keep run parameters in config files so runs remain comparable over time.

Reviewing Eval Logs and Metrics

After a run, look at pass rates, per-example traces, and grader confidence scores. Use these traces to find systematic failures such as prompt sensitivity or hallucinations. Export results to CSV or dashboards for long-term tracking and cross-team discussion.

Using Custom Completion Functions

If you work with non-OpenAI models or local endpoints, provide a custom completion function that handles API calls for those providers. This keeps your eval logic unified while letting you test different backends.

Advanced Topics & Best Practices

CI/CD Integration

Make evals part of your delivery pipeline so model behavior is checked continuously, not just once. Start by defining a compact smoke set that covers critical user journeys and run it on every pull request. This gives quick feedback to developers without slowing reviews. On merges to main, run a larger suite that covers broader slices and edge cases. When a run shows a critical regression, use the result to block the merge and require investigation.

For non-critical metrics, surface them as warnings or annotations so teams can triage without halting progress. Keep runtime predictable by pinning model versions and seeds, and store run metadata with each result so you can trace regressions to a specific change. Finally, treat eval failures like flaky tests: add retries, isolate non-deterministic examples, and continuously refine the smoke set to keep PR checks fast and meaningful.

Comparing Different Model Versions

When you compare versions, focus on paired, per-example diffs rather than only aggregate numbers. Run the identical dataset against both models and save the full responses so you can inspect where outputs diverge.

Look for patterns in the disagreements: does one version become more verbose, more likely to refuse, or more accurate on a certain slice? Use stratified slices to compare performance across categories such as short vs long prompts, safety-sensitive queries, or domain-specific tasks.

Prioritize examples where behavior shifted from correct to incorrect, and use those to guide prompt or dataset adjustments. Keep an experiment log that records model settings, eval config, and example-level changes so you can reproduce and explain why a version change mattered.

Common Pitfalls and How to Avoid Them

Tests that are too narrow or too small give a false sense of security. Avoid examples that lead models to memorize phrasing by varying prompts and including paraphrases. Include negative cases and adversarial inputs so you understand failure modes. Beware of relying solely on pass/fail capture grader confidence and intermediate signals such as token-level similarities or reasoning traces to surface uncertainty. 

Consider the eval suite as code: add reviews for new examples, version datasets, and rotate or retire stale examples to prevent overfitting tests to a particular model. Finally, run periodic audits where humans review a sampled set of failing and passing examples to ensure the evals still reflect real product needs.

Conclusion

OpenAI Evals give teams a practical way to turn expectations into repeatable tests that drive safer, more reliable model behavior. By building small, focused evals for critical user journeys and integrating them into CI pipelines, teams catch regressions early and make releases less risky.

Evals also create a shared measurement language across engineering, product, and QA, so discussions about model choices become data-driven rather than opinion-driven. They are not a silver bullet; pair them with user research, shadow tests, and runtime telemetry to understand real-world impact. 

Maintain your evals by rotating examples, adding edge cases, and tracking per example regressions so tests stay relevant as your models and products evolve. Finally, prioritize safety and fairness in every suite; treat privacy, bias checks, and human review as core evaluation requirements. With a disciplined, eval-first approach, you can move faster while maintaining control and confidence in model-driven features.

You can also connect to our team for guidance on selecting the right platform and setting up your ideal roadmap for specific requirements.

FAQs

What are OpenAI Evals used for?

They are used to systematically test and benchmark language model outputs against expected behavior.

How do you create a custom eval in OpenAI Evals?

Define a Python class that implements evaluator logic, create a dataset, and configure the test in YAML.

Can OpenAI Evals be used with GPT-4?

Yes. Specify the model in your config or CLI and run evaluation against GPT-4 or other supported models

What's the difference between Match and Fuzzy Match templates?

Match requires exact equality. Fuzzy Match allows similar answers by using similarity measures or edit distance thresholds

How do I integrate OpenAI Evals into CI/CD?

Run lightweight evals on pull requests and full suites on merges. Use failing exit codes for critical regressions and non-blocking reports for exploratory metrics.

How much engineering time does eval maintenance require?

Initial setup can take hours to days, depending on complexity. Ongoing maintenance is modest when evals are automated and datasets are reviewed periodically.

Can I use Evals for multimodal models?

Evals focus on text but can be extended with custom completion functions and adapters to evaluate multimodal outputs.

How do Evals compare to observability tools like LangSmith or Weights & Biases?

Evals focus on template-driven testing and grading. Observability tools provide experiment tracking and telemetry. Use both to get full coverage.