Intel / article

Selecting The Right AI Evals Tool

Source: hamel.dev
Author: Hamel Husain
Date: Published Oct 1, 2025

Context

An tool earns its place by making it easier to investigate and fix a failed interaction. Hamel Husain's tool-selection article uses vendors' attempts at the same assignment to examine that workflow: inspect a recorded interaction, annotate the problem, experiment with a change and analyze the results.

This supports the error-analysis process in the field guide. Human review, access to underlying data and export options are among the criteria Husain applies when asking whether a team can investigate beyond a score. The mid-2025 vendor comparisons illustrate those criteria; they do not establish a permanent winner among products.

Key takeaways · AI-distilled

The measure that separates eval tools is time from observing a failure to iterating on a fix. Concretely: can you go from viewing a single trace to experimenting with that same trace in a playground without leaving the tool?
Be deeply skeptical of any feature where an AI writes the evaluation rubric and then immediately scores outputs against it. Stacking those two abstractions hides flaws behind a confident high score.
Error analysis is the highest-ROI activity in AI engineering, so first-class manual annotation beats automation. Most tools still lack axial coding: grouping open-ended failure notes into structured, countable categories.
Braintrust's "money table" is the output pattern to want. Once traces are annotated with failure modes, you get a view you can sort and filter to quantify which failures are actually most common.
Proprietary query languages like Braintrust's BTQL and weak export add friction. Hamel's own setup treats these tools as a backend data store and does the real analysis in Jupyter with custom annotation interfaces.

Terms in this piece · Glossary

eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.

Key quotes

“The best tools don’t try to automate away the human; they empower them.”

“be wary of features where an AI agent both creates an evaluation rubric and then immediately scores the outputs. This “stacking of abstractions” often hides flaws behind a high score.”

“An eval tool should fit your stack, not force you to fit its stack.”

“As for me personally, I tend to use these tools as a backend data store and use Jupyter notebooks as well as my own custom built annotation interfaces for most of my needs.”

Read the source hamel.dev