The Confidence Problem

When I ask AI teams how confident they are about their system's output quality, the room goes quiet. In roughly 70% of my consulting conversations, leaders can't answer that question with any certainty. They've built something, deployed it, and now they're hoping it works.

This reminds me of the early days of my career 16 years ago when automated web testing started gaining traction. Some companies grabbed it and got a real competitive edge. Others ignored it and paid for that choice in production bugs and customer complaints. The same split is happening right now with AI evaluations.

What You'll Learn

In this article, I'll show you how to build an evaluation system that gives you measurable confidence in your AI outputs. You'll learn why most teams overcomplicate this, what the minimum viable evaluation setup looks like, and how to prove ROI on your AI investments to stakeholders who keep asking "is this actually working?".

In projects costing 200K euro or more, I've helped clients in E-commerce and Health reduce output quality incidents by 40-60% within the first quarter of implementing proper evaluation pipelines. And by the way, if you're operating in the EU, this isn't optional anymore. The AI Act requires that your systems be auditable and their behavior understandable by default.

Why Evaluations Are Your Only Real Safety Net

Your AI system produces outputs. Without evaluations, you have no systematic way to know if those outputs are any good. And "any good" here actually means business-good.

Consider this scenario: you've built a chatbot to help customer support agents respond faster. Leadership wants to know if it's worth the 150K euro you spent building it. Are agents actually more efficient? Are customers getting better answers? Without evaluations, you're guessing. With them, you can show that response accuracy improved by 35% and average handling time dropped by 2 minutes per ticket. That's the difference between "we think it's working" and "here's the data proving that."

But there's another angle that matters just as much. Evaluations catch embarrassing outputs before they reach users. Recently, I audited a system for a client where the AI was occasionally generating responses that contradicted the company's own policies. Nobody noticed until a customer complained on social media. A basic evaluation suite would have flagged this pattern in development, not production.

The third benefit is team velocity. This sounds counterintuitive because evaluations feel only like extra work (and yes, this is A LOT of extra work actually). But they function exactly like traditional software tests. Once your team has confidence that they can measure the impact of changes, they move faster. They're not afraid to modify prompts or adjust orchestration logic because they know the evaluation suite will catch regressions. In one engagement, a client's AI team went from shipping updates once a month to twice a week after we implemented proper evaluations. The safety net made that speed possible.

The Practical Framework to Start With Evaluations

Here's where most teams go wrong: they try to automate everything at once. They read about evaluation frameworks and think they need to build something that tests every possible scenario. Six months later, they have an unfinished evaluation system and zero confidence in their AI outputs.

Start smaller. Much smaller.

Step 1: Identify Two Critical Failure Modes

Look at your AI system's outputs over the past month. Where did things go wrong? Where did users complain? Where did you manually override the AI's decisions? Pick the two most business-critical issues or patterns you observed. Not the most technically interesting ones, the ones that cost you money or reputation.

For one EdTech client, this was straightforward: the AI tutoring assistant sometimes went off-topic when students asked personal questions. This is a clear failure mode, with a real business impact. The off-topic responses made their assistant to be perceived as a low-value, general tool instead of something actually bringing value to the users.

Step 2: Build Evaluators That Detect These Specific Issues

An evaluator is just a test that takes an input-output pair and returns a score or a pass/fail judgment. E.g. for the conversation deviation issue, we created a classifier that flags responses touching personal topics.

You don't need fancy infrastructure for this. A Python script that runs against a set of test cases works fine to start. The goal is simply to have an ability to detect those issues.

Step 3: Confirm the Evaluators Catch Real Problems

Run your evaluators against historical data where you know problems occurred. Do they flag the issues you already identified manually? If not, adjust them. If they're catching false positives everywhere, narrow their scope. This calibration step typically takes 2-3 days of focused work. Skip it, and you'll end up with evaluators that generate noise instead of signal.

Step 4: Fix and Verify

Now make changes to address the issues, whether that's prompt modifications, adding guardrails, or adjusting your retrieval pipeline. Then run the evaluators again. Did the issues disappear? Did new issues appear? This feedback loop is where the real value lives.

Step 5: Add to Your Regression Suite

Once an evaluator proves useful, it becomes part of your standard testing pipeline. Run it on every significant change. Over time, you'll accumulate a set of evaluators that represent your team's institutional knowledge about what "good" looks like for your specific AI system.

The 6-Month Cycle Connection

This matters more now than it did two years ago. When model capabilities change every 6 months, when best practices shift quarterly, when your competitors are iterating constantly, you can't afford to move slowly. And you definitely can't afford to move fast without knowing if you're breaking things.

Evaluations are what make rapid iteration safe. They're your permission slip to experiment aggressively while maintaining quality standards. Without them, every change is a gamble. With them, you're making informed bets.

The teams that figured this out early are now shipping improvements 3-4x faster than teams that are still manually reviewing outputs and hoping for the best.

How to Start With Evaluations

This week, pull the last 50 outputs from your most important AI system. Find the 5 worst ones. Ask yourself: what specific, testable condition would have flagged these as problematic before they shipped? Write that condition down. That's your first evaluator specification. You can build it in a day.

Series Navigation

Previous:

• Part 1: Why Your 3-Year AI Roadmap Becomes Obsolete in 6 Months
• Part 2: Stop Hiring: Get 3x Output From Your Current Dev Team

Next in this series:

• Part 4: AI-Native Teams: A Practical Playbook for Engineering Leaders
• Part 5: From Zero to Production-Ready AI in 6 Months

Let's Connect