Evaluations (Evals)
Topic
In machine learning and artificial intelligence, evaluations (commonly referred to as evals) are systematic frameworks and tests used to measure the performance, accuracy, safety, and capabilities of models. They involve assessing how well an AI system or agent performs specific tasks, makes decisions, and interacts with users or other systems. Evals are critical for validating model behavior, identifying failures, and ensuring alignment before deployment.
What experts have said about Evaluations (Evals)
4 statements · 3 positive · 1 neutral
- Nathaniel WhittemorePositiveSep 6, 2026· The AI Daily Brief: Artificial Intelligence News and Analysis
AI evaluations should be treated as core organizational infrastructure.
“making evals core infrastructure”
Open the episode · How to Build an AI-Native Company TodayListen at 0:00
Anthropic treats evaluations as a modern substitute for some PRDs.
“we actually have a saying on the team of evals are the new PRDs”
Open the episode · Anthropic’s first technical PM on token maxing, the jagged edge, and living in the future | Dianne PennListen at 40:59
Evaluations help product teams improve AI user experiences by making quality measurable.
“having things like evals actually is a way not just for folks working on models, but generally within product to get to better user experiences because you can't improve what you can't measure”
Open the episode · Anthropic’s first technical PM on token maxing, the jagged edge, and living in the future | Dianne PennListen at 46:52
A small set of high-quality evaluations can quantify goals, progress, and gaps.
“Just building 10 great evals is important for helping the team quantify what the goal is and what their progress towards it is and what they're missing.”
Open the episode · How Anthropic’s product team moves faster than anyone else | Cat Wu (Head of Product, Claude Code)Listen at 53:31
Statements are attributed to the speaker as said on the episode and reflect their view at the time, not PodLume's. They are not advice.
