A Practical Introduction to Evals for Product Managers Building LLM Features

Written By Alexander Tacho

Large language models have opened up exciting possibilities, from automatic summarization, smart classification, content generation, to workflow automation. But if you've tried building anything with LLMs, you've probably hit the same wall everyone does:

LLM outputs are maddeningly inconsistent.

The same input can produce different answers each time. Quality bounces around. A tiny prompt change can silently break everything. Switch model versions? Good luck predicting what happens.

For product managers, this creates some tough questions:

  • How do you know if an LLM feature is even feasible before you invest in building it?

  • How can you tell if you're making things better or worse during development?

  • How do you keep quality high after launch?

This is where evals come in.

Evals aren't just technical infrastructure for ML engineers. They're becoming essential for anyone building products with LLM APIs. This article explains what evals are, why they matter, and how to actually use them in your product workflow.

What Are Evals?

Evals are a structured way to test and measure the quality of your LLM outputs.

If you're familiar with traditional software testing, here's the analogy: evals are like automated tests, but for AI outputs instead of deterministic code.

Instead of checking if function(x) always returns y, evals answer questions like:

  • Is this answer actually correct?

  • Is the tone right for our users?

  • Does it follow the format we need?

  • Did the model just make something up?

  • Did it skip an important step?

  • Is it consistent when I run it again?

Because LLMs work probabilistically, evals usually measure quality with a score rather than pass/fail. The goal is simple: make AI behavior visible, comparable, and trackable.

Why Evals Matter for Product Managers

Most PMs start by manually spot-checking their LLM features. You paste in a prompt, tweak it, try again, repeat until it looks good enough.

That works fine for early experiments. But it falls apart once you have real users, diverse inputs, and edge cases you didn't think of.

Evals give you visibility and confidence at every stage.

1. Early Validation: Is This Even Possible?

Before you commit engineering resources, use evals to test feasibility:

  • Does the model handle our expected inputs well?

  • Is the accuracy good enough to ship?

  • Are the common mistakes deal-breakers?

  • What's our baseline quality?

Instead of guessing based on a few cherry-picked examples, you can evaluate a realistic sample set and see if the model consistently meets your quality bar. This helps you kill bad ideas early or move forward with confidence.

2. Development: Are We Actually Improving?

During development, literally everything affects output quality:

  • Prompt wording

  • System messages

  • Context window size

  • Which model version you use

  • Temperature and other parameters

Without evals, you're testing these changes manually, inconsistently, and subjectively. With evals, you can:

  • Compare prompts with actual data

  • Track improvement (or regression) across iterations

  • Catch accidental quality drops

  • Get the team aligned on what "good" actually looks like

  • Choose a cost-optimized model that meats quality standards

Think of evals as performance requirements for your AI features, not just vague specifications. Evals can function as concrete, measurable expectations.

3. Production: Is Quality Slowly Degrading?

Here's a risk LLM features have that traditional software doesn't: model providers update their models all the time, often without warning.

An API update can subtly change your outputs. User behavior might shift. An upstream prompt change can cascade downstream. These "silent regressions" can break core flows without throwing a single error.

Evals provide ongoing monitoring:

  • Run them nightly

  • Compare this week's performance to last week's

  • Catch drift before your customers notice it

The result? A more reliable product and way fewer unpleasant surprises.

Start Here: Define What "Quality" Means

Before you can measure quality, you need to define it. Most teams skip this step, which is why they end up with inconsistent results and unclear decisions.

A good quality definition includes:

1. What does a good output look like?

Describe it in plain language:

  • Accurate information

  • Relevant to the question

  • Complete (doesn't miss key points)

  • Appropriate tone

  • Right structure or format

  • Actually helpful to users

2. What does a bad output look like?

List the failure patterns you care about:

  • Hallucinations (making stuff up)

  • Missing critical steps

  • Inconsistent formatting

  • Irrelevant details

  • Unsafe or biased content

3. What matters most to your users?

Prioritize. Not everything is equally important. Sometimes perfect accuracy matters more than tone. Sometimes it's the opposite.

4. How will you measure it?

Start simple:

  • A set of representative test inputs

  • Your expected outputs (or evaluation criteria)

  • A straightforward scoring rubric (even 0-2 is fine)

Once you've done this work, running evals becomes straightforward.

How to run your Evals?

Evals don't require machine learning expertise or expensive evaluation platforms to be run. You can start simple and get more and more sophisticated as you get familiar with Evals and your tooling.

You can start with:

  • A spreadsheet of test cases

  • A simple rating scale

  • Using an LLM to score the outputs (LLM-as-judge)

  • Basic automation added to your CI/CD pipeline later

The tool doesn't matter nearly as much as the discipline of structured measurement.

The Bottom Line

Building LLM features without evals is like shipping software without tests. You might get lucky for a while, but eventually the inherent variability and drift will catch up with you.

Evals give product managers the clarity, confidence, and control you need to build reliable AI features using LLM APIs.

  • Use them early to test if something's even possible

  • Use them during development to guide your iterations

  • Use them in production to maintain quality over time

As LLM integrations become standard in products, evals will become as fundamental as analytics, QA, and user research. If you're exploring LLM features, learning about evals is one of the highest-leverage investments you can make.

Alexander Tacho
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