AI
Evaluation set
Also called
- Eval
- Golden dataset
An evaluation set is the AI equivalent of a test suite. You collect real inputs — actual support tickets, actual claim packets, actual queries — and record what the correct output is for each. Every change to a prompt, model or retrieval strategy is then scored against that set automatically.
Without one, 'the model got better' is an opinion, and the way teams discover a regression is that a customer finds it. With one, you can raise an automation threshold deliberately over time instead of guessing at it, because you can see exactly what accuracy you have at each confidence level.
Building the set is the unglamorous part and the part that determines whether the project works. It usually takes one to three weeks and is the single highest-leverage thing in an AI engagement.
It is what lets you say 'this is 94% accurate on 1,200 real cases' instead of 'it seemed good in the demo' — which is the difference between deploying and not.
Commonly misunderstood
What people get wrong
The claim
“We'll build the evaluation set once it's working.”
What is actually true
You cannot know it is working without one. Teams that defer it are choosing to fly without instruments, and typically discover the problem after go-live rather than before.
Next step
Working through a evaluation set decision?
Tell us the situation. We will give you the tradeoffs as we see them, including when the answer is that you do not need what you are being sold.
No pitch deck. A 30-minute conversation about what you are trying to achieve.