Training & Alignment
Eval (Evaluation)
An eval is the measurement of whether a system solves the task it is meant for, on real cases from your own organization, against a measure fixed in advance. That sounds obvious and is missing from most projects. The reason is not negligence but that generative systems make measuring hard: with a yes-or-no decision, correctness is obvious; with free text, you first have to define what correct even means. That is where the work lies, and it is domain work, not technical work. A model can write the exam; it cannot decide how to recognize that a result holds up. Where this measure is missing, errors are found only by chance. Where it exists, they are found in minutes.
An example
An eval is not software but a table: a hundred rows, each with a real input and the result that would have been correct. Add a rule for what counts as a match. That makes it possible to answer in minutes what otherwise costs weeks of discussion: whether the new prompt is better, whether the cheaper model is enough, whether the update broke something. The effort is in building the table, not in measuring.
A closer look
A usable eval consists of real cases from your own operation, a written definition of “correct,” and a repeatable scoring method. Twenty well-chosen cases say more than a thousand random ones.
Why it matters
The recurring finding from projects: errors almost never arise where a measure is defined. Where measurement exists, they are found in minutes; where it doesn't, they are found by chance or not at all. The real work is therefore not fixing but setting the standard, and no model can take that over. A model can write the exam. How to recognize that a result holds up is a domain decision and stays with humans. As long as “looks good” is the standard, there is none.
Commonly misunderstood
Without an eval, you notice deteriorations only through complaints. It is the only place where a model switch or a change to the prompt can be judged beforehand.
Where this term does its work in the magazine.
4 articles use the term or take up its principle.
On Stage
The Demo and the Tuesday
A good demo makes a claim memorable. It doesn’t make it true.Read the articleFrom Science
Benchmark Performance ≠ Bedside Performance
A good average can hide precisely the cases in which a system becomes dangerous.Read the articleFrom Science
37 Percent of AI Citations Don't Exist
The dangerous error doesn't sound absurd. It sounds exactly the way a good reference should.Read the articleFrom Science