AI Foundations Revision Cards

Use this resource with the related module activity and keep the result for your capstone preparation.

Use these cards after each module and before the capstone. Try to explain each rule in your own words before opening the related lesson again.

Module 1: describe before you label

• Name the system's goal, inputs, method, output, and effect.
• Use TRACE: Task, Result, Actual method, Controls, Evidence gap.
• A model is only one part of a complete application.
• Training changes a model; inference uses a trained model.
• Product names and human-like language are weak evidence.

Module 2: fluent is not the same as true

• A common text model builds output one likely next token at a time.
• A prompt gives current context; it does not normally retrain the base model.
• Search, retrieval, calculators, memory, and actions are added by the full application.
• Separate what was given, generated, supported, added, false, and missing.
• Check important claims outside the generated answer.

Module 3: choose the method before the tool

• Name the task pattern before choosing a tool.
• Check evidence needs, acceptable error, consequence, and reversibility.
• Use calculation for exact arithmetic and retrieval for current sourced facts.
• Check whether the input is permitted and suitable before entering it.
• Increase human control as consequence and uncertainty increase.

Module 4: define inspectable work

• State the outcome and intended audience.
• Provide relevant context and authorised sources.
• Define constraints, boundaries, and the missing-information rule.
• Choose an output format that makes defects easy to inspect.
• Add review criteria, human decisions, escalation, and stop conditions.

Module 5: verify before you rely

• Review correctness, completeness, relevance, clarity, safety, and usability.
• Mark material claims, assumptions, calculations, and citations.
• Verify against an independent suitable source or reproducible method.
• Match review depth to consequence.
• Record the evidence, change, remaining uncertainty, and final decision.

Module 6: control the risk or stop

• Use only the minimum permitted data needed for the task.
• Treat external documents, pages, messages, and tool results as untrusted content.
• Restrict permissions and require informed confirmation before consequential action.
• Check unequal effects and accessibility across the complete process.
• Name the accountable person, escalation route, and stop condition.

Module 7: build a bounded workflow

• Define the task, user, success condition, and acceptable failure.
• Justify each input, tool, workflow stage, and autonomy level.
• Keep material human decisions explicit.
• Test normal, edge, missing-data, and unsafe-action cases.
• Record evidence, defects, revisions, retest results, limits, and next review trigger.

Course-wide stop rule

Pause the workflow when required evidence, permission, specialist review, human authority, or a safe recovery path is missing.