From my consulting work, I estimate that 70% of teams building AI systems have zero systematic testing in place during early development. Not because they don't value evals, but because they're stuck choosing between frameworks, debating statistical significance, or architecting an LLM-as-judge pipeline they'll never complete (guaranteed).

The cost? Weeks of development time lost to paralysis, or worse, shipping completely blind to production.

What You'll Learn

In this quick guide, I'll show you how to build a working eval system in an afternoon using just 50 datapoints and manual review. After delivering 5 production AI systems for clients across EdTech and E-commerce, I've found this approach consistently outperforms sophisticated frameworks that exist only in planning documents. You'll walk away with a concrete method to start testing your AI systems today, not next quarter.

The Perfectionism Trap

Here's what I typically find when I audit AI projects: teams waiting for conditions that will never arrive. They want the perfect eval framework (and spend weeks comparing options or designing custom solutions). They want statistical significance from large datasets (important later, irrelevant now, at the start). They want automated LLM-as-judge pipelines (a genuinely hard problem that shouldn't be your first attempt).

And while they wait, one of two things happens. Either they delay shipping indefinitely, or they push to production with no testing at all because "proper evals take too long."

What you can find on the internet doesn't help either. It's flooded with detailed tutorials on building sophisticated evaluation systems, written for teams that already have dedicated evaluation expertise. If you're just starting, these resources make the problem feel impossibly complex.

The 50-Datapoint Method

A dataset of 50 datapoints with manually reviewed outputs will systematically beat any eval system that doesn't exist yet. I've used this approach in every AI system I've shipped, and it works because it prioritizes signal over sophistication.

Here's what this looks like in practice: you build a "regression dataset" you can review in about 15 minutes after each prompt change or AI system modification. For a recent client project, this simple check caught three breaking changes in the first week alone, each of which would have degraded production quality by an estimated 40% on affected queries.

What you get from this approach:

Immediate feedback on what broke. When you change a prompt and run your 50 examples, you know within minutes if something went wrong. No waiting for batch jobs or statistical analysis.

Pattern recognition through consistency. The same person reviewing outputs repeatedly starts spotting failure modes faster. They notice when the system starts hedging too much, or when it loses a specific capability. This intuition is worth more than a dashboard full of metrics you don't understand yet.

Confidence to ship. When your 50 examples pass review, you have a concrete reason to believe your change is safe. It's not certainty, but it's infinitely better than guessing.

What You Don't Need (Yet)

At the beginning, you can skip:

• Complex evaluator chains with multiple LLM calls scoring each response (aka LLM-as-a-judge). These are expensive to run, hard to calibrate, and often produce scores that don't correlate with actual reality.

• Teams of subject matter experts reviewing every output. One person with product context is enough. Your product manager or the developer who owns the feature makes an ideal reviewer because they already understand what good output looks like.

• Perfect ground truth datasets with hundreds or thousands of examples. You'll build toward this over time, but requiring it upfront guarantees you'll never start or spend weeks just assembling them.

Building Your First Dataset

Start with three categories of examples:

Happy path cases. The straightforward inputs where your system should clearly succeed. These form your baseline and help you catch regressions.

Negative examples. Inputs where you expect the system to refuse, redirect, or handle gracefully. Edge cases, out-of-scope requests, adversarial inputs.

Synthetic data if needed. If you don't have real user data yet, create representative examples based on your product requirements. These can be replaced with actual production data as it flows in.

As you run your system in production, keep adding to this dataset. Every bug report becomes a test case. Every surprisingly good response gets added to your positive examples. Within a few weeks, you'll have a dataset that reflects the actual distribution of inputs your system handles.

From Manual to Automated

Yes, this process is completely manual. And that's expected.

The person doing the reviews builds intuition about what exactly your evals need to measure. They start noticing patterns: "the system always fails on questions about pricing," or "responses get worse when the input is longer than 500 words." After a couple of weeks, they can articulate specific failure modes and their priority.

This knowledge is what you need before you can build automated evals. You can't write a test for a failure mode you haven't identified yet. The manual review phase is how you discover what to test, not a workaround until you figure out the "real" solution.

Your Next Step

Create a spreadsheet with three columns: input and expected behavior (you can even skip this one because it's already in your reviewer's head). Add 20 happy path examples, 20 negative examples, and 10 edge cases. Run them through your system and review the results. The whole process should take less than two hours, and you'll have more eval coverage than most teams achieve in months of eval framework debates.

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