Make AI answers reliable enough to use
Expose the context, define acceptance, test difficult cases, and diagnose whether failures come from input, evidence, instructions, capability, or handoff.
BEFORE LESSON ONE
Bring real material so the course produces a real result.
- 01 · WHO IT IS FOR
- For teams whose prompt works in a demo but changes quality across real inputs.
- 02 · WHAT TO BRING
- Bring one prompt, three good outputs, and three disappointing outputs.
- 03 · HOW TO FINISH
- Each failed case is assigned to a fixable category instead of a vague quality complaint.
WHEN THIS COURSE FITS
Start here when any of these situations is true.
You do not need a level label. Enter when the work shows one of these signals, bring real material, and use course acceptance to decide when you are done.
- 01✓
The demo works but real inputs produce variable answers
A live version exists, yet quality changes with documents, users, or formats.
- 02✓
Every failure leads to another prompt edit
The team does not separate missing context, conflicting acceptance, model capability, and broken handoff.
- 03✓
A model switch is proposed without a fixed baseline
You need the same task slices and failure labels to judge whether a change is actually better.
COURSE MILESTONES
Every stage leaves a change someone else can review.
These milestones are not another reading list. They show whether the lessons form one piece of work: real material at the start, connected artifacts in the middle, and evidence another person can reproduce at the finish.
- 01 BEFORE↓
Freeze the current version before changing the prompt.
Save the live prompt, three satisfying outputs, three disappointing outputs, and the complete input that produced each one. This becomes the baseline for judging improvement.
Starting evidence: every good and bad case can be reproduced with its original context. - 02 MIDPOINT↓
Turn quality complaints into diagnosable failures.
Map context, write the acceptance contract, and build an evaluation set across real task slices. Record omissions, conflicts, weak evidence, capability limits, and broken handoffs separately.
Midpoint evidence: every acceptance rule has at least one case and one failure label. - 03 FINISH✓
Rerun the same set and look for newly introduced regressions.
After fixing the leading failure source, compare against the frozen baseline. Define when changing inputs, dependencies, or human overrides should trigger another evaluation.
Completion evidence: gains trace to a change, failures have causes, and drift signals have owners.
- 01✓LESSON 1
Map the context before rewriting the prompt
Separate instructions, source material, examples, history, tools, and output rules to see what the model actually receives.
LESSON OUTPUT · context mapNot completeOpen lesson → - 02✓LESSON 2
Write the acceptance contract before the prompt
Define required fields, evidence, tone, uncertainty, and rejection conditions before optimizing prompt wording.
LESSON OUTPUT · acceptance contractNot completeOpen lesson → - 03✓LESSON 3
Run a ten-case AI pilot before you scale
Build a deliberately small test set with ordinary, difficult, ambiguous, and unsafe cases so a promising demo becomes evidence.
LESSON OUTPUT · pilot sheetNot completeOpen lesson → - 04✓LESSON 4
Build an AI evaluation set that stays useful
Sample real task slices, write reviewable references, calibrate graders, and version the set so model changes produce trustworthy comparisons.
LESSON OUTPUT · evaluation-set blueprintNot completeOpen lesson → - 05✓LESSON 5
Diagnose an AI answer before changing the model
Trace a bad answer to missing input, conflicting instructions, weak evidence, capability limits, or a broken handoff.
LESSON OUTPUT · diagnostic treeNot completeOpen lesson → - 06✓LESSON 6
Monitor an AI workflow before drift becomes an incident
Watch task mix, inputs, dependencies, outcomes, and human overrides against a named baseline so every alert leads to an inspectable response.
LESSON OUTPUT · drift watchboardNot completeOpen lesson →
COURSE ACCEPTANCE
Each failed case is assigned to a fixable category instead of a vague quality complaint.
Reading is not the finish line. Assemble the lesson outputs and ask another person to review them against the standard above.