Use AI evals to take the guesswork out of prompts and instructions

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I’ve been using AI in my workflows for months. When creating agents, I’d try a few instructions, make some tweaks, and start using them. It worked well enough since I reviewed everything before use, but the results weren’t always consistent. I uncovered practices for writing better instructions, which helped, but I still found myself reworking outputs when things didn’t hit the mark.

That’s where AI evals come in. They’re a way to test and refine AI systems to ensure outputs are reliable and consistent. By incorporating evals, I’ve drastically improved the agents I work with, making them more dependable and scalable.

Here’s what AI evals are, why they matter, and how you can use them to refine your AI workflows.

What Are AI Evals?

An evaluation (eval) measures how well your AI systems work. Think of it as a grading rubric for your AI:

Input: The task or prompt (e.g., “What’s the capital of France?”).

Expected Output: The ideal answer (e.g., “Paris”).

Evaluation Logic: A way to check if the answer matches the ideal (e.g., “Is the answer ‘Paris’?”).

Evals test if your system produces accurate, high-quality results.

Examples include:

Logic-Based Answer Checking: “Is the JSON valid?”

Model Grading: “Was this joke funny?”

Why Evals Are Essential

1. Improving Consistency

Evals ensure your workflows are predictable. Small inconsistencies in outputs can derail processes, especially as workflows scale. For example, I use evals to test my research agent's ability to analyze trends from forums and reviews. Without evals, I’d spend hours catching and fixing edge cases manually.

2. Scalability Without Sacrificing Quality

If your AI workflows handle diverse inputs—like summarizing reviews or generating insights—evals help maintain quality across tasks. Whether you’re scaling AI for internal use or for clients, evals act as a quality checkpoint that keeps everything on track.

3. Faster Iteration and Better Results

Evals let you test different prompts, models, and workflows systematically. This saves time and eliminates guesswork, enabling you to iterate quickly and confidently. When building integrations or refining AI tools, evals give you clarity on what works best—without endless trial and error.