Lab
AI Usage Lab
Practical playbooks for testing AI models before production use — prompt tests, structured-output checks, long-context trials, multimodal trials, automation-risk reviews, and regression checks. Paste-ready Markdown templates are included. Learn → Apply → Verify → Test.
The lab extends the curriculum from Learn → Apply → Verify into Test. Each playbook is a testing recipe you run yourself before integrating a model; each template is a paste-ready Markdown planning document you adapt to your workload.
Lab workflow
Step 1
Define task →Name the workload, the acceptance rubric, and the data gaps.
Step 2
Build test set →Pick representative prompts (or assets) from real traffic and pin a fixed schema.
Step 3
Run model trials →Execute the playbook against each candidate model with parameters held constant.
Step 4
Record evidence →Roll observations into an evidence brief and store the canary suite for regression checks.
Playbooks
6 testing playbooks
Each playbook walks one testing dimension — prompt behaviour, structured output, long-context, multimodal, automation, regression — and ends with a Markdown evidence brief you can attach to /briefs/build.
- beginner25 min
Prompt testing basics
The minimum prompt-testing routine to run against a shortlisted model before integration. Defines a representative prompt set, structured observations, and concrete failure modes — no benchmark scores.
Outputs: 2 artifacts · Templates: 2
Open playbook →
- intermediate30 min
Structured output testing
How to validate JSON mode, structured output, and tool calls against your real schema before depending on the model in a pipeline.
Outputs: 2 artifacts · Templates: 2
Open playbook →
- intermediate35 min
Long-context testing
How to test long-prompt behaviour past the catalogue's verified context window — recall, instruction adherence, and cost growth — without trusting a marketing number.
Outputs: 2 artifacts · Templates: 2
Open playbook →
- intermediate30 min
Multimodal input testing
How to test image, audio, video, and PDF input channels against your real assets — never against marketing copy.
Outputs: 2 artifacts · Templates: 2
Open playbook →
- intermediate40 min
Automation workflow testing
How to test a model inside an automation loop — chained prompts, retries, downstream parsers, regression surface — before letting it run unattended.
Outputs: 2 artifacts · Templates: 2
Open playbook →
- intermediate30 min
Model regression testing
How to run a small, repeatable canary suite after every snapshot rotation so silent regressions surface before production traffic notices.
Outputs: 2 artifacts · Templates: 2
Open playbook →
Prompts
Evaluation prompt library
Six prompt sets — summarisation, structured extraction, long-context recall, instruction following, refusal boundary, automation robustness. Evaluation inputs you run in your own harness, not production prompts.
Summarization quality
Beginner · 20 min · faithful summarisation.
Structured extraction
Intermediate · 25 min · schema-conformant extraction.
Automation robustness
Intermediate · 25 min · contract adherence in automations.
New here? Start at /lab/prompt-testing-basics or read the evaluation guide.
Templates
3 paste-ready templates
Generic Markdown planning documents you adapt per workload. Every template is exportable via /api/lab/templates/<slug>.
Template · markdown
Model evaluation plan
A blank evaluation plan for a single model + workload pairing. Use one copy per candidate.
5 sections · paste-ready Markdown
View template →
Template · markdown
Prompt test matrix
A row-per-prompt matrix you fill in per candidate model. Pair with the model evaluation plan.
4 sections · paste-ready Markdown
View template →
Template · markdown
Automation risk checklist
A pre-launch risk checklist for automations that depend on a model. Pair with the automation workflow testing playbook.
5 sections · paste-ready Markdown
View template →
What the lab does not promise
- No production readiness guarantee. A passing playbook is evidence, not approval.
- No compliance or regulatory certification. Verification is not certification.
- No safety validation. Templates and playbooks are planning tools, not safety reviews.
- No model ranking. The lab does not score candidates against each other.
- No benchmark replacement. The lab teaches your own testing discipline; it does not publish synthesized benchmark numbers.