eval-suite-bootstrap

slug	eval-suite-bootstrap
name	Eval Suite Bootstrap
version	0.1.0
description	Bootstrap a minimal evaluation suite for an LLM-powered feature.
category	evaluation
tags	["evals","llm","testing","benchmark"]
inputs	[{"name":"feature","type":"string","required":true,"description":"LLM feature to evaluate"},{"name":"criteria","type":"string","required":false,"description":"What good output looks like"}]
output	{"format":"markdown","description":"Eval suite structure with dataset, judges, and CI integration."}
author	badhope
license	MIT
created	"2026-06-21T00:00:00.000Z"
updated	"2026-06-21T00:00:00.000Z"

When to use

Shipping an LLM feature, moving from prototype to production, or standardizing evals.

Inputs

Describe the feature and quality criteria.

Output

A bootstrap eval suite plan with examples.

Prompt

Bootstrap an LLM evaluation suite.

Output:
1. Evaluation types: unit, pairwise, human-in-the-loop
2. Dataset: 20-50 examples covering happy path, edge cases, regressions
3. Judges: deterministic assertions, LLM-as-judge rubrics, human labels
4. Metrics: accuracy, relevance, safety, latency, cost
5. CI integration: when to run evals, gating criteria
6. Example eval case with input, expected behavior, and rubric

Keep it minimal but production-ready.

When NOT to use

• Experiments that will be discarded within a week
• Features where ground truth is undefined or subjective without human review
• Teams without access to model outputs in a reproducible environment

Example

Input:

feature: 'support ticket auto-reply'
criteria: 'accurate, empathetic, does not promise refunds'

Output:

## Eval Types
- Unit: deterministic checks (reply contains ticket ID, no refund promise).
- LLM-as-judge: rate empathy and accuracy 1-5.

## Dataset
30 tickets across refund, bug, account categories.

## CI
Run on every prompt change; fail if accuracy < 85% or safety violations > 0.

Footguns

These are the bugs that bite every new user. Check them before shipping:

• Evals too easy: Test cases that all pass immediately don't catch regressions.

  • how to detect: eval suite passes on day one with no model improvements
  • how to fix: include hard edge cases, not just happy path examples

• Noisy LLM-as-judge: LLM judges with vague rubrics give inconsistent scores.

  • how to detect: same output gets different scores on different runs
  • how to fix: use deterministic assertions where possible, keep LLM judge rubrics specific

• Test data leakage: Training data that overlaps with evaluation examples.

  • how to detect: model performs unusually well on eval but fails in production
  • how to fix: keep eval data separate, regularly refresh eval data

• Evals not blocking releases: CI passes but evals aren't enforced as gates.

  • how to detect: bad model versions reach production
  • how to fix: make evals required in CI pipeline, block on threshold breaches

• Metrics not aligned with user experience: Optimizing for automated metrics that don't reflect actual quality.

  • how to detect: high accuracy scores but users complain
  • how to fix: include human evaluation, track user feedback alongside metrics