How to know if your AI feature is any good — the skill that separates hobby projects from shipped products
An evaluation (eval) is a systematic way to measure whether your AI system produces good output. Without evals, you are guessing. You change a prompt and it seems better — but is it actually better on the inputs that matter? Did it improve for one case and break three others?
Evals are the single most underrated skill in applied AI. Every production AI system that works has an eval suite behind it. Every AI demo that looks impressive but ships poorly lacks one.
Different AI features need different evaluation approaches.
An eval set is a collection of inputs and expected outputs that represent your real use case. Start small — 50 examples is enough to begin. Make sure the examples cover edge cases, not just the easy cases. Include examples where the correct answer is 'I don't know' or 'this is outside my scope'.
The eval set should come from real user queries, not from your imagination. If you do not have real queries yet, use synthetic ones but replace them with real data as soon as possible.
The best AI teams work like this: write the eval first, then iterate on the prompt/RAG/model until the eval passes. This is the AI equivalent of test-driven development.
Every time you change a prompt, run the eval suite. Track scores over time. Set a minimum threshold and do not ship below it. This discipline prevents the cycle of 'change the prompt, break something else, change it again'.
Testing only on easy cases — your eval set should include the hard cases that actually fail in production. Overfitting to the eval — if you keep tweaking prompts to pass specific eval cases, you end up with a prompt that works on the eval but not on real inputs. Not versioning your evals — when you change the eval set, you lose the ability to compare with past results. Using only automated metrics — some things require human judgement.
Collections of test inputs and expected outputs
Using a model to evaluate another model's output
Measuring accuracy vs completeness
Comparing two versions with real users
Ensuring changes do not break existing behaviour
Standard datasets for comparing model performance
PromptFoo
Open-source prompt testing and comparison tool
LangSmith
Tracing and evaluation for LLM applications
Braintrust
Evaluation platform for AI products
RAGAS
Evaluation framework specifically for RAG systems
Weights & Biases
Experiment tracking including eval metrics
Understand why evals matter (this guide)
Build a simple eval set of 50 examples for a task you care about
Write a script that runs your prompt against the eval set and scores results
Try LLM-as-judge for open-ended outputs
Set up automated eval runs that trigger on prompt changes
Expand your eval set with real user queries over time
