Kit · automation specialists
Automation workflow testing kit
Prepare a safe testing workflow for AI-powered automation. Walks the automation-specialist learning path, the structured-output + pricing-references + testing lessons, three exercises, the automation workflow testing + regression playbooks, the automation-robustness + structured-extraction prompt sets, and the automation risk checklist + prompt test matrix templates.
Audience
automation specialists
Difficulty
intermediate
Estimated time
200 min · 9 steps
Who this kit is for
Source-backed shortlists, structured-output testing, automation risk checklists, and prompt evaluation sets for automation builders, SEO operators, and technical consultants. The platform teaches careful, source-backed AI use inside workflows — never an automation marketing pitch.
Goal
End with a safe model-use checklist, a written external test plan with regression cadence, and a decision brief that ships alongside the automation runbook.
What you will produce
- Automation risk checklist
- Prompt test matrix with per-candidate observations
- Safe model-use checklist for the pipeline under review
- External test plan
- Decision evidence brief
Prerequisites
Export Markdown
The kit serialises to a single Markdown document you can paste into a design doc, ticket, or PR description.
Workflow
9 sequenced steps
Open each step in order. Every step opens a route that already exists — no parallel UI.
Step-by-step workflow
Walk the automation specialist learning path
Read the path's five lessons + four exercises so structured output, pricing references, and the testing framework land before integration.
Open
/learn/path/automation-specialist→Output: Notes on structured output, pricing references, lifecycle, testing.
Build the first shortlist
Complete the build-first-shortlist exercise to capture candidate slugs for the automation step.
Open
/learn/exercises/build-first-shortlist→Output: Shortlist URL the team can re-open before each automation release.
Review the pricing reference
Capture provider/unit/retrievedAt for the candidate pricing rows your cost projection depends on.
Open
/learn/exercises/review-pricing-reference→Output: Pricing-reference note that finance can sanity-check.
Run the automation workflow testing playbook
Map the pipeline end-to-end, run shadow jobs, capture retry behaviour + tail latency + parser interaction.
Open
/lab/automation-workflow-testing→Output: Shadow-run observations and a canary suite that detects regressions later.
Run the automation-robustness prompt set
Surface contract drift across allowed categories, missing-value handling, retry decisions, exact-string fallbacks.
Open
/lab/prompts/automation-robustness→Output: Per-prompt observations recorded with exact-string adherence noted.
Fill the automation risk checklist + prompt test matrix
Adapt both Markdown templates to your pipeline and paste observations from the previous steps.
Open
/lab/templates/automation-risk-checklist→Output: Filled-in risk checklist + matrix attached to the runbook.
Schedule a regression suite
Walk the model-regression-testing playbook to freeze the canary suite, wire alerting, and document the regression cadence.
Open
/lab/model-regression-testing→Output: Documented regression schedule plus the canary suite itself.
Generate the decision evidence brief
Open /briefs/build with the candidates the automation will call and export Markdown.
Output: Markdown brief paired with the automation runbook.
Write the external test plan
Complete the plan-external-model-test exercise and capture the regression cadence.
Open
/learn/exercises/plan-external-model-test→Output: Written test plan that ships with the runbook.
Required resources
The kit reuses existing surfaces — no parallel UI, no duplicated content. Open each surface in the order the timeline lists.
Lessons
Structured output, JSON mode, and tool use →
The difference between structured output, JSON mode, and tool/function calling — and what is currently verified in the catalogue.
AI model pricing references explained →
Why catalogue pricing rows are references, not quotes — and how to read them without ranking models by price.
How to test an AI model before integration →
After the shortlist: how to run your own prompt, latency, rate-limit, cost, and compliance tests — using the evidence brief as the pack you ship to reviewers.
Exercises
Build your first source-backed shortlist →
Pick a use case, filter the catalogue by verified fields, and end with a shortlist URL you can share with the team.
Review a pricing reference safely →
Open a verified pricing row, read its unit semantics + retrieval date, and walk the reverification queue if it is stale.
Plan an external model test →
Use the brief and the testing lesson to plan your own prompt, latency, rate-limit, and cost validation work for the shortlist.
Lab playbooks
Automation workflow testing →
How to test a model inside an automation loop — chained prompts, retries, downstream parsers, regression surface — before letting it run unattended.
Model regression testing →
How to run a small, repeatable canary suite after every snapshot rotation so silent regressions surface before production traffic notices.
Evaluation prompt sets
Automation robustness →
Evaluate whether a model handles automation-style constraints (allowed categories, missing values, retry decisions, ambiguity flags) without silently breaking the contract.
Structured extraction →
Evaluate whether a model extracts fields into a requested structure without inventing missing values or breaking schema constraints.
Final checklist
- Pipeline scope mapped end-to-end.
- Shadow-run observations recorded with retry behaviour + parser interaction.
- Automation risk checklist filled and attached to the runbook.
- Canary suite frozen and the regression cadence documented.
- Decision evidence brief paired with the runbook.
- External test plan written.
Caution: No persistence — the checklist resets on every visit. Capture progress in your own notes.
Evidence routes
What this kit does not promise
- Guarantee automation reliability.
- Improve search-engine traffic or organic rankings.
- Substitute for human review on a customer-facing surface.
- Approve the pipeline as production-ready.
What workflow kits do not promise
- No model recommendations or rankings. Kits walk the evidence; the reader's team makes the decision.
- No live pricing quotes. Pricing rows referenced inside the kit are sourced references with retrievedAt dates.
- No production-readiness guarantee. The kit ends with an external test plan — running those tests is the team's responsibility.
- No compliance certification, legal advice, or vendor endorsement.
- No SEO ranking guarantees or automation reliability guarantees.
- No accounts, no progress tracking, no course-completion certificates. Completion is the Markdown artifacts the kit puts in your hands.