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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
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Apply stage
7
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Verify stage
7
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Test stage
15
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Package stage
14
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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.
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I want to choose model candidates
Build a source-backed shortlist.
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I want to compare models side by side
Render verified fields against each other.
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I want to test model behaviour
Run prompt + structured-output + regression tests.
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I want to evaluate prompts
Six generic, safe evaluation prompt sets.
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I want to document evidence
Package the decision brief or evaluation plan.
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I want to review sources
Audit citations + freshness across the catalogue.
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I want to prepare a governance review
Source freshness + lifecycle + refusal-boundary suite.
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I want to test an automation workflow
Validate model behaviour inside an unattended loop.
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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: difficulty: beginner. Canonical URL stays /resources; this filtered URL is noindex,follow.
Learn · 11 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 →
Apply · 2 resources
Exercise · Apply
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.
BeginnersDevelopersProduct teamsShortlist URLOpen →
Exercise · Apply
Compare context windows without ranking models
Use the comparison builder to render verified context window + max output tokens for 3–4 candidate models side by side.
DevelopersProduct teamsComparison URLOpen →
Verify · 3 resources
Exercise · Verify
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.
Product teamsAutomation specialistsOpen →
Exercise · Verify
Inspect lifecycle before integration
Pull lifecycle state for a candidate model, check for retirement date, and add a migration target to your notes if one exists.
Product teamsGovernance teamsLifecycle review noteOpen →
Exercise · Verify
Check source freshness and reverification state
Open the sources hub for a provider, identify any stale citations, and walk the reverification queue to see what is due for re-check.
Governance teamsSource freshness checklistOpen →
Test · 3 resources
Lab playbook · Test
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.
DevelopersProduct teamsAutomation specialistsPrompt test matrixOpen →
Evaluation prompt set · Test
Summarization quality
Evaluate whether a model summarises without adding unsupported claims, omitting constraints, or inventing numbers.
DevelopersProduct teamsPrompt test matrixOpen →
Evaluation prompt set · Test
Instruction following
Evaluate whether a model honours formatting, word-count, uncertainty, and forbidden-phrase instructions without silent drift.
DevelopersProduct teamsPrompt test matrixOpen →