How to work with Evals and A.I

2026/02/23•Pedro Uzcátegui

If you aren't measuring your AI's performance, you are just guessing.

Building applications with Large Language Models (LLMs) is easy. Making them reliable is hard. The difference between a cool weekend project and a production-ready AI system comes down to one thing: Evals.

Evaluations (or "evals") are the automated tests of the AI world. They help you measure whether a change to your prompt, model, or retrieval pipeline actually improved the system or broke it.

Why Evals Matter

When you change a prompt to fix a specific edge case, how do you know you didn't break 10 other things?

Without evals, you rely on "vibe checks"—manually testing a few inputs and seeing if the output looks okay. This doesn't scale. Evals give you a quantitative metric to track over time.

Types of Evals

There are generally three ways to evaluate LLM outputs:

  1. Deterministic Evals: These are traditional unit tests. Does the output contain a specific substring? Is the output valid JSON? Is the length under 500 characters?
  2. Model-Graded Evals (LLM-as-a-Judge): Using another LLM (usually a larger, more capable one like GPT-4 or Claude 3.5 Sonnet) to grade the output of your system based on a rubric.
  3. Human Evals: Having domain experts review the outputs. This is the gold standard but is slow and expensive.

Building Your First Eval Suite

Start small. Don't try to build a massive, comprehensive test suite on day one.

1. Collect a Golden Dataset

Gather 20-50 real-world examples of inputs and their ideal outputs (or at least the criteria for a good output). This is your baseline.

2. Define Your Metrics

What matters for your application?

  • Accuracy: Is the information factually correct?
  • Tone: Is it polite and professional?
  • Formatting: Did it follow the requested JSON schema?

3. Automate the Pipeline

Write a script that runs your dataset through your LLM pipeline and scores the outputs. You can use frameworks like Braintrust, LangSmith, or just a simple Python script.

The LLM-as-a-Judge Pattern

When using an LLM to grade another LLM, you need to be very explicit in your grading prompt.

You are an expert evaluator. Grade the following response based on accuracy.

Input Question: {question}
Expected Answer: {expected_answer}
Actual Response: {actual_response}

Score the response from 1 to 5, where 5 is perfectly accurate and 1 is completely wrong.
Output ONLY a JSON object with the following schema:
{
  "score": number,
  "reasoning": "string"
}

Conclusion

Evals are not optional if you want to build serious AI products. Start with deterministic checks, build a small golden dataset, and gradually introduce model-graded evals as your application grows. Stop guessing and start measuring.