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Outcome

LLM prompt evaluation

A product-led path through the evaluation prompt library — six generic, safe prompt sets that surface failure modes (hallucination, schema drift, lost-in-the-middle, instruction skipping, unsafe compliance, contract violation). Run them in your own harness; the catalogue never calls live models on your behalf.

Outcome headline

Evaluate model behaviour with safe, structured prompt sets

Problem

  • Teams compare AI models by 'vibe' instead of by observed behaviour.
  • Production prompt drift after a snapshot rotation is invisible without a canary set.
  • Public benchmark scores are not reproducible enough to drive selection on their own.
  • There is no shared baseline of evaluation inputs the team can rerun consistently.

Who this is for

  • Teams that need a small repeatable prompt set for evaluating candidate models.
  • Reviewers comparing two candidates on faithful behaviour rather than benchmark headlines.
  • Operators preparing a regression suite for unattended production use.

What you will produce

Completion is the named Markdown artifacts in your hands — not a certificate, badge, or progress bar.

  • Per-set Markdown export (via /api/lab/prompts/<slug>)
  • Prompt test matrix (Markdown)
  • Per-prompt observation record
  • Canary suite for regression checks

Workflow

5 sequenced steps

Open each step in order. Every route already exists in the product — outcome pages are entry points, not parallel surfaces.

Suggested workflow

Open each step in order. Every route already exists — no parallel UI, no duplicated content.

  1. Read the evaluation guide

    Open /lab/evaluation →

    Output: Framing for what evaluation means here.

  2. Open the evaluation prompt library

    Open /lab/prompts →

    Output: Pick the set that matches your behaviour to test.

  3. Open the prompt-test matrix template

    Open /lab/templates/prompt-test-matrix →

    Output: Markdown scaffold for per-candidate observations.

  4. Run the prompts in your own harness

    Open /learn/testing-ai-models →

    Output: Per-prompt observations recorded verbatim.

  5. Add findings to the decision brief

    Open /briefs/build →

    Output: Brief paired with the prompt-set observations.

Routes into the product

Each entry opens an existing route. The outcome page is a product entry point, not a parallel surface.

What this outcome does not promise

  • Score the model for you.
  • Replace your own red-team or safety audit.
  • Predict production behaviour from 5–10 prompts.
  • Substitute for benchmark methodology where independent reproducibility is required.

What outcome pages do not promise

  • No model recommendations, no winner claims, no rankings. Outcome pages route the reader through evidence; the reader's team decides.
  • No live pricing, no live status, no fabricated benchmark scores or latency numbers.
  • No production-readiness guarantee, no compliance certification, no automation reliability guarantee.
  • No SEO ranking guarantees. The outcome label exists so the right team can find the workflow, not as a search promise.
  • No accounts, no progress tracking, no course-completion certificates. The artifact list above is the completion signal.