LEARNING PROGRESS / FOUR PRACTICAL COURSES

See your learning as clearly as a course syllabus.

See completed lessons, the next lesson, and working artifacts by course, then continue without finding your place again.

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AI VISTA / PROGRESS FILEOVERVIEW

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01Build your first testable AI workflow
02Make AI answers reliable enough to use
03Design answers that can show their evidence
04Automate with permission, recovery, and cost controls

0/ 16 LESSONS COMPLETE

COURSE PROGRESSWORKING ARTIFACTSACHIEVEMENT RECORD

LEARNING ROUTE / STAGE 06 · PROGRESS

This stage reconnects reading progress to the work you actually produced.

Completion percentage is only an orientation aid. The useful questions are which artifacts exist, which lesson comes next, and which decision still needs another course or community review.

BRING: Completed lessons, artifacts, and discussion feedback
DO HERE: Review completed work and the next move by course
LEAVE WITH: A progress overview and a clear continuation point

WHEN THIS STAGE IS DONE Return home with the next task Close the loop, then begin again from new real work.

FROM COMPLETION TO WORK EVIDENCE

A complete body of work is not a lesson count. It is four kinds of material another person can review.

Course states help you find your place. The evidence portfolio shows whether learning has reached real work. When one kind is missing, do not restart; return to the matching course and fill the earliest gap.

REVIEW RULEShow the artifacts without a verbal explanation. They count as work evidence only if the reviewer can still identify the decision, support, and failure handling.

01
TASK
Which exact action belongs to AI?
MATERIAL
Fit matrix, task brief, and data boundary
REVIEW
Can a colleague identify the input, acceptable output, prohibited action, and handoff owner?
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02
ACCEPTANCE
What result passes, needs revision, or must be rejected?
MATERIAL
Context map, acceptance contract, evaluation set, and diagnostic tree
REVIEW
When the same cases run again, can failures be classified consistently and traced to a change?
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03
EVIDENCE
Why should a consequential claim be believed?
MATERIAL
RAG decision gate, source register, and citation-freshness audit
REVIEW
Does each citation support the claim, use a current version, and respect access scope?
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04
RECOVERY
How does failure stop and return to a safe state?
MATERIAL
Permission ladder, human gates, recovery runbook, cost map, and drift watchboard
REVIEW
Can the team rehearse a timeout, duplicate action, or rejected approval and choose continue or rollback?
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COMPLETION IS NOT MASTERY

Use four questions to see whether learning has reached the real work.

A completed state only says you visited the lesson. Real completion needs an artifact, a reviewable standard, current evidence, and a recovery path. A missing answer does not reset progress; it identifies the next place to learn.

HOW TO USEAfter each course, ask someone who did not build the workflow to challenge each row. Record what you cannot answer, then return to the linked topic.

01
DOES THE ARTIFACT EXIST?
Can you open a completed brief, evaluation sheet, permission map, or recovery runbook—not merely recall the guide?
Pass signal: the artifact contains real input from the current task.
Return to the course and make it →
02
DID ACCEPTANCE PASS?
Can a colleague use the same standard and reach the same pass, revise, or reject judgment on the cases?
Pass signal: the standard separates acceptable, revisable, and rejected work.
Rebuild acceptance and cases →
03
IS THE EVIDENCE STILL VALID?
Are the sources behind important decisions still current, correctly versioned, and permitted for this user?
Pass signal: consequential claims trace to valid sources.
Review source, version, and freshness →
04
CAN THE WORKFLOW RECOVER?
When a model, tool, or handoff fails, who notices, where does it stop, and how does it return to a safe state?
Pass signal: the team has rehearsed at least one failure.
Complete the stop and recovery path →