You've built a groundbreaking AI agent. It’s powered by the latest LLM, the prompts are meticulously engineered, and in your ad-hoc tests, it performs brilliantly. But here's the billion-dollar question: how do you really know it’s ready for production? How can you be confident it won't falter on an unusual customer query, hallucinate incorrect facts, or drift in quality after a minor update?
The answer doesn’t lie in more ad-hoc testing. It lies in your data. Specifically, the quality of your evaluation dataset is the single most important factor in determining the reliability of your AI functions, workflows, and agents.
At Evals.do, we believe in quantifying AI performance with code. A core part of that philosophy is treating your test data as a first-class citizen. This article provides a practical framework for creating, curating, and managing high-quality datasets to rigorously test your AI components.
In traditional software testing, unit tests and integration tests provide a safety net. For AI, your evaluation dataset serves the same purpose—and more. It’s the benchmark against which all changes are measured.
The principle of "Garbage In, Garbage Out" is doubly true for AI evaluation. A weak, biased, or incomplete dataset gives you a false sense of security. You might celebrate a 95% pass rate, only to discover your AI fails spectacularly on common edge cases once it interacts with real users.
A high-quality dataset, on the other hand, allows you to:
Your evaluation dataset, often called a "Golden Set," is the source of truth for your testing. It should be a curated collection of inputs and their ideal, or "golden," outputs. Here are the essential characteristics of a robust Golden Set.
Your test data must mirror the real-world scenarios your AI will encounter. If you're building a customer support agent, your dataset should be filled with authentic customer queries, not abstract questions. Sourcing data from production logs (anonymized, of course) is an excellent starting point.
A good dataset covers the full spectrum of expected (and unexpected) inputs. It should include:
For each input, you need a "golden" answer. This is your benchmark for what "good" looks like. For objective tasks, this might be a single correct answer. For subjective tasks, it’s often a rubric. For example, a golden response for a support query might be evaluated on:
Creating a powerful evaluation dataset is a systematic process of sourcing, curating, and structuring data.
The best datasets are a blend of different sources. Don't rely on just one.
This is where raw data becomes a high-quality test case. For each input sourced in Step 1, you need to define the ideal output.
This often involves a human-in-the-loop. An expert reviews the input and writes the "perfect" response. This response becomes the ground truth.
A single test case in your dataset might look like this in a simple JSON structure:
{
"input": "I was charged twice for my subscription this month, can you fix this?",
"category": "billing_error",
"golden_response": {
"text": "I'm very sorry to hear about the double charge. I've located the duplicate transaction and have already issued a full refund for it. You should see it back in your account within 3-5 business days. Can I help with anything else today?",
"expected_actions": ["find_duplicate_charge", "issue_refund"]
}
}
Your evaluation dataset is a living asset. As your product evolves, so should your tests. The most effective way to manage this is to treat your dataset like source code, a practice often called Evaluation-Driven Development.
This is where the magic happens. A well-structured, versioned dataset is the fuel for an automated evaluation engine like Evals.do.
Our platform is designed to integrate seamlessly into this workflow. By making a simple API call within your CI/CD pipeline, you can run your AI component against your entire evaluation set.
{
"evaluationId": "eval_abc123",
"target": "customer-support-agent:v1.2",
"dataset": "customer-support-queries-2024-q3",
"status": "completed",
"summary": {
"overallScore": 4.35,
"pass": true,
"metrics": {
"accuracy": { "score": 4.1, "pass": true },
"helpfulness": { "score": 4.4, "pass": true },
"tone": { "score": 4.55, "pass": true }
}
}
}
Evals.do takes your target (the AI agent) and runs it against each item in your dataset. It then uses AI-powered or rule-based graders to score the agent's performance against the "golden response" on metrics you define—like accuracy, helpfulness, and tone.
The result is a quantifiable, objective summary of AI quality. You move from "I think it works" to "It scores a 4.35 overall, passing our thresholds for production readiness."
Stop guessing about your AI's performance. The path to building truly reliable AI services starts with a commitment to rigorous, data-driven evaluation. By treating your test dataset as a critical, version-controlled asset, you create a powerful safety net that catches regressions, highlights weaknesses, and provides the quantitative evidence needed to deploy with confidence.
Ready to turn your data into a cornerstone of AI quality? Explore Evals.do and start quantifying your AI performance with code.