LabaliciousAI & QA ACADEMY
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Same standard, different projects — mappings and reviewer calibration

These are fictional teaching examples, not assessments of real learners or evidence that the rubric has been validated. Use AIQAA-1.0 and the agreement. All 21 criteria stay required; the product-specific evidence changes. The examples below are abbreviated mappings, not complete approved agreements.

Three right-sized products

Product Three connected features Meaningful state and useful output Explicit exclusions
Shift Garden — volunteer scheduler Define shifts; assign fictional volunteers; filter and summarize uncovered places Shifts, volunteers and assignments; saved capacity and uncovered counts No real people, notifications, accounts or payroll
Pantry Pilot — meal planner Maintain ingredient stock; plan recipes; calculate a shopping list Ingredients, recipes and planned meals; stock edits recalculate shortages No medical/nutrition advice, purchases or live store APIs
Quest Ledger — study-project tracker Create tasks; record study sessions; summarize weekly progress Tasks, sessions and goals; durations and completion affect summaries No surveillance, real student records or predictive grading

Example acceptance cases to agree before building

Shift Garden: A shift has capacity two and no assignments. Assign V01 and V02; the UI and persisted data show two assignments and zero places remaining. Attempt V03; reject it with a useful message and unchanged state. Reload; the assignments remain. Import a file with an unknown shift ID; reject it without replacing the valid schedule. Capacity zero is either valid or invalid according to the agreed domain rule—reviewers must not guess.

Pantry Pilot: Stock contains three eggs. Two planned recipes require two eggs each. The shopping list shows one egg needed; after stock changes to four it shows zero. Reject a negative stock entry without modifying the saved value. Export, reset and reimport; recipe links and quantities match the original state. Specify units and rounding in the agreement; do not invent a unit-conversion requirement during review.

Quest Ledger: Task T01 has no sessions. Add a 25-minute session dated inside the agreed week; the total becomes 25. Add a 20-minute session; the total becomes 45. A negative duration is rejected without altering either session. Define the week boundary/time zone, then verify an entry just outside it is excluded. Repeated import follows the agreed duplicate rule, not an assumed one.

Each project also needs its remaining feature, UI, boundary, recovery and handoff cases; these examples alone are insufficient for P1.

Criterion mapping across all three

ID Shift Garden Pantry Pilot Quest Ledger
P1 Shift creation, assignment, uncovered view Stock, meal plan, shortage calculation Task creation, session entry, weekly view
P2 Reload schedule; export/reset/restore; duplicate assignment Preserve quantities/recipe links; invalid import Preserve sessions; boundary totals; repeated import
P3 Reject unknown volunteer/shift IDs; label no-auth boundary Reject malformed quantities/file data Reject invalid dates/durations; no claim of student privacy controls
V1 Consistent schedule/assignment/summary design; two iterations Consistent stock/plan/list design; two iterations Consistent task/session/report design; two iterations
V2 Keyboard assignment and narrow-screen schedule Keyboard stock edit and narrow shopping list Keyboard session entry and readable weekly summary
V3 Empty schedule, full shift, rejected assignment, successful save Empty pantry, invalid quantity, successful plan, recovery No sessions, invalid time, saved entry, import recovery
Q1 Capacity, lost assignments, misleading uncovered count Wrong shortage, mixed units, corrupted restore Wrong week total, duplicate sessions, lost progress
Q2 An actual capacity or persistence defect found during work An actual calculation or restore defect found during work An actual date/filter or duplicate defect found during work
Q3 Retained behavioral checks, local and CI release runs Retained calculation/recovery checks, local and CI Retained session/boundary checks, local and CI
H1 Plan → assign one volunteer → investigated fix → release Plan → one meal/stock slice → investigated fix → release Plan → one logged session → investigated fix → release
H2 Kickoff, working slice and release review responses Same stages, with actual product feedback Same stages, with actual product feedback
H3 Explain actual assignment/design decisions and their evidence Explain stock/unit/design decisions and evidence Explain week/session/design decisions and evidence
G1 Linked issue/branch/PR/fix/check/merge/tag Same workflow, product-specific links/diffs Same workflow, product-specific links/diffs
G2 Explain own PR, review response and release navigation Same competencies using own repository Same competencies using own repository
G3 Clean focused source; attributed assets; fictional volunteers Clean source; attributed recipes/assets; no real purchases Clean source; attributed assets; fictional study records
A1 A bounded scheduler task with checked output A bounded quantity/plan task with checked output A bounded session/filter task with checked output
A2 Observed AI mistake, correction and independent verification Same evidence pattern; no invented AI mistake Same evidence pattern; no invented AI mistake
A3 Two workflow variants on a fixed small assignment task Two variants on a fixed small shortage task Two variants on a fixed small date-filter task
B1 Synthetic coverage analysis → decision memo → update Synthetic ingredient analysis → memo → update Synthetic session analysis → memo → update
B2 Another person sets up, checks and resets the tagged app Same clean handoff on documented platform Same clean handoff on documented platform
B3 File-exchange/failure map and honest schedule release decision File-exchange/failure map and honest planning release decision File-exchange/failure map and honest tracker release decision

A smaller model or plain JavaScript can satisfy every row. A costly model, elaborate architecture or hosted integration satisfies none by itself. For Q2/A2, the named defect types are possibilities, not instructions to manufacture discoveries. The rubric's preapproved, openly labeled project negative-control route can demonstrate verification without pretending the learner encountered a natural bug or model error. It never replaces history or GitHub evidence.

Borderline anchors: rate the condition, not the impression

For calibration, assume other rows have valid evidence only where explicitly stated. “Overall” below means readiness for Jared's decision, never an automatically issued credential.

Scenario Correct row/overall treatment Why / next step
All required outcomes hold; reviewer prefers purple to the agreed green Do not fail V1 on this preference Inspect consistent use and actual readability; optional art direction is coaching
Beautiful final app, but one final upload and no corroborating intermediate work H1 unverified; overall pending Request real prior artifacts or plan further genuine iterations; never synthesize historical commits
Fifty commits and three “LGTM” comments, no substantive staged feedback H2 not yet demonstrated after inspection Counts do not meet the stated condition; arrange genuine artifact-specific reviews and iterations
No suitable naturally occurring bug, but a preapproved project negative control genuinely fails the behavioral assertion and the correct revision passes Q2 can be demonstrated after inspection Verify the declared route and real outputs; do not record this as a natural discovery or accept a setup-error failure
Four preserved milestone snapshots and substantive PR discussions, squash merges on main H1 can be demonstrated after inspection Squashing is not a failure if the real progression is traceable
Required save result disappears on reload; all other rows demonstrated P2 not yet demonstrated; overall not yet demonstrated A disclosed defect still contradicts the agreed persistence requirement
Reviewer cannot obtain the approved sandbox because the lab host is down Affected rows unverified; overall pending Restore the environment; do not claim the software failed or the learner lacks the skill
README setup fails in the exact promised clean environment due to a missing dependency B2 not yet demonstrated Fix instructions/dependencies and repeat the handoff
No user accounts, but approved untrusted-import cases and controls match the implementation P3 can be demonstrated Do not invent an OAuth requirement at the final review
A score of 100 from an accessibility scanner, but keyboard focus cannot reach Save V2 not yet demonstrated The actual keyboard condition outranks the tool's score
One unusually good demo, but the required suite alternates pass/fail Q3 not yet demonstrated Investigate inconsistency; a chosen green run is not dependable verification
Premium model generated the product; learner cannot locate or explain their own reviewed diff yet G2 not yet demonstrated after supported observation AI use is allowed; locate the real artifacts and build understanding through further coached work
Quiet learner uses notes and screen magnification to explain the actual diff and evidence accurately G2 can be demonstrated Fluency, charisma and unaided recall are not conditions
Another reviewer disputes whether a required error message permits recovery Overall pending until resolved Preserve both findings; reproduce the agreed error/recovery case; Jared records the resolution
One optional illustration is unevenly aligned, but all design-sheet and usability conditions hold May defer as cosmetic Do not treat every visual preference as a failure; still record the improvement
All rows demonstrated, coursework complete, approved agreement, presentation reviewed, no holds Eligible for Jared's decision Jared still separately authorizes certification; the record is not an issuer
Product evidence is complete but an assigned course activity remains outstanding Not yet eligible; completion outstanding separately Agree a make-up/equivalent activity; do not rerun already demonstrated product work

Run a reviewer calibration session

  1. Independently rate these anchors before discussing them. For an authentic pilot, replace the short descriptions with the same frozen fictional project/evidence packet for both reviewers; a narrative exercise alone cannot measure real-world agreement.
  2. Compare criterion state, observation and next action—not a numerical grade. Do not show one reviewer's judgment to the other before their initial decision.
  3. For a disagreement, identify whether the rule was ambiguous, evidence differed, a case was not reproducible or the judgment was based on taste. Preserve both initial records and the evidence-based resolution.
  4. Apply the double-review sampling plan in the rubric. Track per-row and overall initial agreement, reasons for disagreement, review time and learner evidence burden privately.
  5. Jared approves any clarifications. Material changes receive a new prospective version; no silent change to a learner's agreed bar. No calibration scores or real credential decisions are published.

© 2026 Jared Cluff.