How I Use OpenAI Evals to Test GPT Prompts Before Shipping AI Features

The Critical Need for Testing AI Outputs

Working as a creative technologist and AI consultant, I've learned that testing AI outputs is non-negotiable. When you integrate GPT-powered features into client-facing products, a lot can go wrong if you don't rigorously vet the model's responses.

Unlike traditional software (where a function either works or it doesn't), AI can produce almost-right answers or unexpected quirks. Even small prompt tweaks or model updates can introduce new errors. In fact, even minor changes often require re-testing the entire system to ensure stability and avoid regressions.

If you deploy without proper checks, you risk shipping an AI that might give incorrect information or behave inconsistently. And in production, that means a hit to your AI product quality and user trust.

The stakes are especially high for client-facing features. You might A/B test a new GPT-driven feature and see a positive conversion lift, but traditional A/B testing alone can miss subtle AI failures. For instance, your conversion rate might be up, but what if your chatbot is subtly insulting 5% of your customers or hallucinating fake discounts? A standard A/B test won't catch those subtle issues until brand damage is already done.

I've seen firsthand that relying on "it seems to work" isn't enough – we need systematic evaluation. That's why I turned to OpenAI Evals, a framework purpose-built to check GPT's outputs before real users ever see them.

Finding a Solution in OpenAI Evals

OpenAI Evals is an open-source toolkit (now also available as a hosted API) that lets you test your GPT prompts and workflows systematically. Think of Evals as unit tests for AI, but with fuzzy logic.

Instead of relying on gut feeling or a quick spot check, you can feed the model a series of prompts (your test cases) and automatically verify whether the outputs meet your expectations. OpenAI's President, Greg Brockman, even said "Evals are surprisingly often all you need" – and in my experience, they have indeed become a cornerstone of ensuring AI reliability.

What makes Evals special is that it provides objective, reproducible metrics for model performance. It shifts your mindset from "Does this AI output look okay?" to "Is it correct according to a defined standard?".

For example:

• If I expect a prompt to yield an answer "2008", I can have an eval automatically check if the model's response contains "2008"
• If I need the model to output valid JSON, I can write an eval that attempts to parse the JSON – if parsing fails, the eval flags it

Evals can handle both straightforward checks and nuanced ones. In cases where what counts as "correct" is subjective (say, testing if a summary captures the key points), Evals even supports model-graded checks: essentially using a stronger model to grade the output against an ideal answer. This two-stage approach lets you evaluate things like tone or clarity by asking the model-as-grader to score the answer's quality.

Critically, OpenAI Evals makes it easy to catch problems before they reach production. With each new model update or prompt change, I run my evals suite. This has saved me from bad deploys more than once. It provides a safety net so that I'm not just guessing that a GPT-powered feature will behave – I know it meets the criteria we care about.

As one OpenAI guide notes, developing strong evals leads to a more stable, reliable application that's resilient to model changes. You can even integrate Evals into your CI/CD pipeline to prevent launches if the AI's accuracy drops below a threshold. In short, Evals gives me confidence in the quality of AI outputs, similar to how automated tests give software engineers confidence in code. It's become part of my standard toolkit for ensuring high AI product quality.

Setting Up Evals: My Workflow

Let me walk through how I typically set up OpenAI Evals in practice. It's straightforward and requires minimal overhead – a bit like writing a few unit tests for a new piece of code.

1. Prepare a Dataset of Prompts and Expected Outputs

I start by collecting example inputs and the ideal outputs I expect from the model. These examples cover both typical cases and edge cases. I put them into a JSONL (JSON Lines) file, where each line is a JSON object with an input and ideal field.

For instance, if I'm testing a summarization prompt, my dataset might look like:

{"input": "Artificial Intelligence enables machines to learn from data.", "ideal": "AI allows computers to learn from data."}
{"input": "Testing AI models requires careful evaluation.", "ideal": "AI model testing needs structured evaluation."}

This file serves as the ground truth for what the model should output for each prompt.

2. Define the Evaluation Logic

Next, I decide how to grade the model's answers against the ideal outputs. OpenAI Evals provides handy eval templates for common patterns.

For tasks with a single correct answer, I might use a basic string match or numeric comparison (e.g., did the model's answer exactly match "42"?). For more open-ended tasks, I might use a model-graded eval, where GPT-4 (for example) compares the model's answer to the ideal and judges if it's correct.

There are ready templates, so often I just write a small YAML config selecting the appropriate eval class. In simple cases, no coding is needed – I just point the eval to my JSONL file and specify the checking method. In more custom scenarios, I might write a short Python eval function (for example, to parse JSON output or measure semantic similarity). But generally, the framework covers most needs out of the box.

3. Run Evals via the CLI

With the dataset and eval definition in place, I run the evaluation using the command-line tool. All it takes is a single command:

oaieval gpt-4 my_eval_name

This instructs the evals framework to use GPT-4 on the "my_eval_name" test I set up. The CLI spins through all my test prompts, queries the model, and checks each response against the ideal answers. After a few seconds, I get a report – for instance, it might say something like "Running eval on 20 samples… 18/20 correct, Accuracy = 90%".

I can dive into the detailed results to see which prompts failed and why. If I prefer, I can also run evals programmatically via the API or even as part of a script, but the CLI tool is convenient for quick runs.

4. Integrate and Iterate

If the eval results show any failures or low scores, I treat it as an opportunity to improve. Maybe the prompt needs refining, or perhaps I should switch to a more capable model for that task. I'll iterate on the prompt or logic and re-run the eval until I'm satisfied with the performance.

Over time, I expand my eval dataset as I discover new edge cases or as requirements change. This growing suite becomes a regression test bed for the AI. Whenever I upgrade to a new model version or make changes, I rerun the evals to ensure nothing important broke. It's a continuous feedback loop that accompanies development.

By following these steps, setting up evals feels less like a chore and more like an integral part of building AI features. The first time might take a bit of effort (mostly thinking through what "correct output" means for your use case), but after that, it's rinse and repeat. And the payoff in confidence and avoided mistakes is huge.

Use Case: GPT-Powered CRO Insights (Optimizely + Evals)

To illustrate how this works in a real scenario, let me share a use case from the world of Conversion Rate Optimization (CRO) – an area I often work in.

Imagine we have an experimentation tool like Optimizely running an A/B test on a website, and we want to use GPT to automatically summarize the A/B test results for the product team. Instead of manually parsing analytics, the GPT-based feature would generate a quick narrative: e.g. "Variant B outperformed Variant A with a 5% higher conversion rate, likely due to the clearer call-to-action messaging."

It sounds handy – and it is – but without testing, this could go sideways. We're asking GPT to interpret data and communicate a conclusion. What if it misunderstands the numbers or writes a misleading summary? In CRO, a bad insight is worse than no insight, because it could send the team down the wrong path.

Here's how I approached this with OpenAI Evals as a safety net:

Defining Expectations

First, I defined what a "good" summary looks like. For each possible outcome (A wins, B wins, or no significant difference), I wrote an ideal summary. The ideal outputs included the correct identification of the winning variant and a mention of the key metric difference. These formed my eval dataset.

For example, one ideal answer might be: "Variant B had a higher conversion rate than Variant A (15% vs 10%), indicating B is more effective at driving sign-ups." Each test case also provided the model with the input data (the results stats).

Automated Checks

I then created an eval that checks two things in the model's summary:

1. Does it correctly identify the winning variant?
2. Does it mention the numbers or factual info correctly?

This is a mix of string matching and semantic checking. I could have the eval search the output for the correct variant name and verify certain numbers are present. For nuance (like ensuring the explanation isn't hallucinated), I could even employ a model-graded eval that asks, "Is this summary factually consistent with the data provided?" using a trusted model as the judge.

Essentially, the eval acts as an automatic proofreader that flags any summary that doesn't align with the actual A/B results.

Running Before Shipping

When we first built this feature, we ran these evals and discovered a few surprises. In one case, GPT-4 worded the summary in a confusing way that could be misinterpreted. In another, a tweak to the prompt caused the model to occasionally omit the actual conversion numbers. These issues were caught in eval testing – not by an end user or client.

We iterated on the prompt until all our eval cases passed, ensuring the summaries were always accurate and clear. Only then did we green-light this feature for real users.

In Production Monitoring

Even after deployment, I keep the eval and periodically run it, especially if we update the model or adjust the prompt. This way, if a regression sneaks in (say a future model update changes how it responds), we'll catch it before any faulty summaries go out. It's like an early warning system.

In fact, this practice aligns with the idea that every time an LLM app changes – be it prompt or model – you should re-evaluate it end-to-end. I've integrated this eval into our experimentation workflow; it runs offline whenever we prepare a new experiment template or adopt a new model version, acting as a guardrail against GPT misreading our Optimizely test data.

This CRO example highlights a broader point: for any AI feature that generates user-facing content or decisions, Evals can ensure the AI stays on-brand, factual, and effective. Whether it's summarizing test results, generating product descriptions, or powering a chatbot, I use Evals to verify critical aspects (did it follow the guidelines? did it stay factual? was the tone correct?).

It's far better to catch an issue in a controlled eval than to have your AI feature quietly doing the wrong thing in production. As experimenters, we still run A/B tests to measure impact, but we run Evals to measure quality and correctness. We need both to confidently roll out AI-driven innovations.

Preventing Regressions and Bad Deploys

One of the biggest benefits I've found with OpenAI Evals is preventing regressions – those sneaky degradations that can happen when you change something in your AI system. In traditional software, you wouldn't dream of shipping a major update without running your test suite. I believe the same discipline should apply to AI systems. Evals have become my go-to method for AI quality assurance.

There have been times where a prompt that worked well yesterday suddenly started performing poorly after a model update. (OpenAI's models are continuously improving and changing under the hood, which is great – but it means yesterday's prompt might need tweaking today.) Because I had evals in place, I was immediately alerted to a drop in accuracy on a specific task after an upgrade.

This early catch meant we could adjust our approach or hold off on deploying that update. As OpenAI's documentation emphasizes, developing a suite of evals lets you quickly understand how a new model version will perform for your use case. I've made it a habit that before any significant change goes live – whether it's switching from GPT-3.5 to GPT-4, or modifying the system prompt – I run the relevant evals.

If the results don't meet our benchmark (for example, if accuracy falls below, say, 90% on critical evals), that change doesn't ship until we resolve the issue. It's basic AI hygiene.

We also treat eval results as a key metric in our deployment checklist. Just like you wouldn't release software with failing unit tests, we don't release AI features with failing evals. This practice has saved us from bad deploys, like preventing a flawed content generator from going live when it started outputting off-brand language in one of our tests.

Catching that in staging with Evals meant avoiding an embarrassing apology to users later. In another case, an eval helped reveal a subtle drop in the quality of explanations our AI was providing – something that might not have been immediately obvious without a side-by-side comparison.

Because evals give quantitative feedback (e.g. "pass rate" or a score), it's easy to track improvements or declines over time. We even log these metrics over each release to ensure our AI product quality is trending in the right direction.

An added bonus is the confidence it gives to stakeholders (and to me!). When a client asks, "How do we know the AI won't do X?", I can literally show them our eval tests and how the model performs. It's transparent and reassuring to demonstrate that we've thought about failure modes and have checks in place. Evals provide that extra layer of trust, which is crucial when you're an AI consultant delivering solutions that might have seemed like black boxes otherwise.

Conclusion: Evals as Basic AI Hygiene

In the fast-paced world of AI development, I've found that adopting OpenAI Evals is one of the best decisions for ensuring AI product quality and reliability. It has become a basic hygiene practice for me – just like version control or unit testing in software engineering.

By systematically testing GPT prompts before they hit production, we can iterate faster and sleep easier at night. The peace of mind that comes from having a robust eval suite is hard to overstate.