Build a Test Suite for a Non-Deterministic AI Feature
Grade probabilistic output without brittle snapshot tests.
/ quick answer
Test structure deterministically, quality with rubrics, and regressions with stored real failures. Grade probabilistic output without brittle snapshot tests.
- 01Layer 1 — structural: schema validity, required fields, no forbidden content. Hard assertions.
- 02Layer 2 — behavioural: does the answer contain the required facts? Keyword and semantic checks.
- 03Layer 3 — graded: a model scores against a rubric; assert on the score threshold, not the text.
- 04Build the regression set from real production failures, never from invented examples.
- 05Run layers 1–2 on every commit; run layer 3 nightly (it costs tokens).
- 06Track pass rate over time as a release gate, and never ship a drop without explanation.
What does the Build a Test Suite for a Non-Deterministic AI Feature workflow do?
Test structure deterministically, quality with rubrics, and regressions with stored real failures.
What problem does Build a Test Suite for a Non-Deterministic AI Feature solve?
Traditional assertions fail on model output that is correct but worded differently.
How many steps does Build a Test Suite for a Non-Deterministic AI Feature take?
6 steps. It starts with layer 1 — structural: schema validity, required fields, no forbidden content. hard assertions. and ends with track pass rate over time as a release gate, and never ship a drop without explanation..
Which tools does Build a Test Suite for a Non-Deterministic AI Feature need?
It uses ai-testing-stack — each linked below with its own node.
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Related concepts
The vocabulary this page depends on.
- →AI Testing
AI testing covers two things: using AI to generate and maintain tests, and testing AI systems whose output is non-deterministic.
- →AI Evaluation
AI evaluation is the measurement layer of an AI system: a fixed set of cases, a scoring method and a tracked pass rate you can regress against.
- →AI Evals
Reproducible test suites that measure LLM output quality across model, prompt and code changes.
- →Evals
Automated tests that grade LLM outputs against expected behavior.
Related workflows
Turn this into a repeatable process.
- →Build an AI Code Review Loop
Catch what agents get wrong before a human reads the PR.
- →Build an Eval Suite Before Optimising Prompts
Stop guessing whether a change improved anything.
- →Run a Coding Agent on a Real Codebase Safely
Give an agent write access without letting it wreck main.
- →Build an Internal Knowledge Bot
Ship a Slack bot that answers questions from your company docs.
Related tool stacks
The tools that run it in production.
- →AI Testing Stack
Test deterministic code and probabilistic AI output in one pipeline.
- →Coding Agent Stack
Run coding agents with executable feedback and reviewable diffs.
Related prompts
Reusable prompts for this job.
- →AI Test Generation Prompt
Generates behavioural tests and edge cases, not tautologies.
- →AI Code Review Rubric Prompt
A reviewer prompt that finds real defects instead of style opinions.
- →Coding Agent Task Spec Prompt
Writes the task brief that keeps a coding agent inside scope.
- →Test Case Generator Prompt
Generate meaningful test cases from a function signature.
Related use cases
How people apply it, and what came out.
- →Two-Founder Startup Ships Like a Team of Six
Coding agents plus a hard verification gate replaced three planned hires.
- →Platform Catches a 19% Quality Drop Before Users Did
Continuous sampling and evals caught silent degradation after a model update.
- →Build An AI Crypto Research Agent
A read-only research agent produced daily briefings on a 30-token watchlist, cutting a 90-minute manual routine to a 10-minute review.
- →Build An AI Trading Assistant
An assistant that drafts and attacks its own trade plans raised plan completeness to 100% and rejected a fifth of setups on liquidity grounds.
Comparisons & alternatives
Pick between the options.
- →Coding Agent vs Autocomplete Copilot
Copilots accelerate typing; coding agents complete tasks. Different unit of work, different review burden.