Why Pass/Fail Beats Numeric Scales
Understand how to design effective evaluations by using pass/fail judgments instead of numeric scales to improve clarity and accelerate model improvement. Explore the role of error analysis, real failure tracking, and targeted automation to build focused evaluation workflows that align with actual system behavior.
So far, the focus has been on observing system behavior, including capturing traces, generating intentional inputs, and reviewing examples to understand how the system fails in practice. At this stage, many teams reach a transition point. They have concrete failures, a rough taxonomy, and a growing intuition for quality. The next question is how to turn this understanding into evaluations that drive system improvement over time.
This lesson focuses on evaluation design and methodology. The emphasis is on the choices that determine whether evaluations clarify reality or obscure it, rather than on metrics, dashboards, or tools. Poorly designed evaluations slow teams down, create false confidence, and divert attention from genuine failures. Well-designed evaluations do the opposite. They force clarity, accelerate iteration, and protect against regressions without becoming a burden.
Why most teams should start with pass/fail judgments?
When teams first formalize evaluation, many gravitate toward one to five rating scales because they feel more nuanced than a simple yes or no. In practice, numeric scales introduce ambiguity precisely when clarity is most crucial. The difference between a three and a four is rarely well defined, even for a single reviewer, and across multiple reviewers, the numbers quickly lose shared meaning.
What makes pass/fail judgments more aligned with real product decisions?
Pass or fail judgments force a concrete decision: would you be comfortable shipping this to a user? This ...