Find
Resource finder
Find the lesson, exercise, lab playbook, prompt set, kit, or evidence workflow that matches your role and task. Every link below opens an existing surface — the finder routes you in, it does not recommend a model.
Total resources
62
Lessons, exercises, paths, lab tools, kits, outcomes, audiences, demos, workspaces, evidence examples.
Learn stage
19
Open filtered view →
Apply stage
7
Open filtered view →
Verify stage
7
Open filtered view →
Test stage
15
Open filtered view →
Package stage
14
Open filtered view →
Next step
I want to…
Each card opens a filtered view of the resource finder — the canonical URL stays /resources and filtered URLs are noindex,follow.
I want to learn the basics
Plain-language concept lessons.
Open filtered view →
I want to choose model candidates
Build a source-backed shortlist.
Open filtered view →
I want to compare models side by side
Render verified fields against each other.
Open filtered view →
I want to test model behaviour
Run prompt + structured-output + regression tests.
Open filtered view →
I want to evaluate prompts
Six generic, safe evaluation prompt sets.
Open filtered view →
I want to document evidence
Package the decision brief or evaluation plan.
Open filtered view →
I want to review sources
Audit citations + freshness across the catalogue.
Open filtered view →
I want to prepare a governance review
Source freshness + lifecycle + refusal-boundary suite.
Open filtered view →
I want to test an automation workflow
Validate model behaviour inside an unattended loop.
Open filtered view →
Stage map
Learn → Apply → Verify → Test → Package
Every product surface lives at exactly one stage. The graph counts the resources at each stage so the reader can scan where the next step lives.
Step 1
Learn
Plain-language concept lessons + audience entry points + role-based learning paths.
Step 2
Apply
Exercises + selection / comparison workspaces + guided demos that produce a working URL.
Step 3
Verify
Source freshness + lifecycle inspection + reverification queue + coverage audit.
Step 4
Test
Lab playbooks + evaluation prompt sets the reader runs in their own harness.
Step 5
Package
Decision brief + Markdown templates + workflow kits + outcome flows that ship a paste-ready artifact.
Filters
Reset all filters →Every filter is a link — no client state, no accounts, no progress tracking. Filtered pages are noindex,follow; the canonical URL is /resources.
Goal
Resource type
Evidence artifact
Difficulty
Results
19 resources
Filtered view: stage: Learn. Canonical URL stays /resources; this filtered URL is noindex,follow.
Learn · 19 resources
Lesson · Learn
How to choose an AI model
A workflow for picking which AI model to test next — start from your use case, inspect verified fields, export an evidence brief.
BeginnersDevelopersProduct teamsOpen →
Lesson · Learn
Context windows explained
What a context window means, what it does not guarantee, and which verified fields to inspect before assuming a model fits your prompt.
BeginnersDevelopersOpen →
Lesson · Learn
Hosted vs first-party AI models
Why the model creator and the billing provider are usually different, and how the catalogue keeps the two separate.
BeginnersDevelopersProduct teamsHosted-provider noteOpen →
Lesson · Learn
AI model pricing references explained
Why catalogue pricing rows are references, not quotes — and how to read them without ranking models by price.
Product teamsAutomation specialistsOpen →
Lesson · Learn
Model lifecycle: active, deprecated, retired
What active, preview, deprecated, and retired mean for a model — and why lifecycle should gate integration decisions.
Product teamsGovernance teamsLifecycle review noteOpen →
Lesson · Learn
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.
DevelopersAutomation specialistsExternal test planOpen →
Lesson · Learn
Multimodal input: image, audio, video, PDF
How the catalogue records which models accept image, audio, video, or PDF input — and why marketing copy is not enough to assume support.
DevelopersProduct teamsOpen →
Lesson · Learn
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.
DevelopersAutomation specialistsOpen →
Lesson · Learn
Status-aware model selection
Why vendor-reported status pages and independent probes are kept separate — and when status should gate a model decision.
Governance teamsAutomation specialistsOpen →
Lesson · Learn
Why benchmark scores can mislead
Contamination, prompt variance, version drift, and why the catalogue does not publish provider-reported benchmark scores casually.
BeginnersDevelopersProduct teamsOpen →
Learning path · Learn
AI model basics for careful users
Three foundational readings + four practical exercises. Walks from a use case to a paste-ready evidence brief plus a freshness checklist. Built for readers new to the catalogue.
BeginnersOpen →
Learning path · Learn
Technical model evaluation before integration
Four readings + three exercises + two pre-seeded workflows. Walks the verified fields a developer needs (hosted creator vs host, modality channels, structured generation, the testing framework) and ends with a comparison URL, an evidence brief, and a written external test plan.
DevelopersDecision briefOpen →
Learning path · Learn
Model selection for product use cases
Four readings + three exercises + one pre-seeded workflow. Walks use-case framing, pricing references, lifecycle gates, and benchmark limits so the team can align on a defensible review — ending with a use-case shortlist, a pricing-reference note, a lifecycle risk note, and the evidence brief.
Product teamsDecision briefOpen →
Learning path · Learn
AI model governance and source review
Four readings + three exercises + three audit workflows. Walks lifecycle, status, benchmark limits, pricing, and source freshness so a reviewer can sign off with a defensible evidence trail — never a certification claim.
Governance teamsSource freshness checklistLifecycle review noteOpen →
Learning path · Learn
Safe AI model use for automation workflows
Five readings + four exercises + three pre-seeded workflows. Built for people wiring AI models into automations: structured outputs, prompt cost projections, regression test plans, and a brief that ships with the automation runbook. Never an automation marketing pitch.
Automation specialistsAutomation risk checklistExternal test planOpen →
Audience · Learn
For developers
Evaluate AI models the way you evaluate any other infrastructure
DevelopersOpen →
Audience · Learn
For product teams
Turn a product use case into a defensible model decision
Product teamsOpen →
Audience · Learn
For automation specialists
Use AI models inside automations without over-trusting them
Automation specialistsOpen →
Audience · Learn
For governance teams
Build a defensible AI model review with sourced evidence
Governance teamsOpen →