AI Foundations

Understand modern AI capabilities and limits, specify tasks clearly, verify claims, protect confidential data, and build reusable, safe workflows. A grounding course for anyone who works with AI tools and wants judgement, not hype.

Register no.

LH-02

Status

15 lessons published · open to enrol

Level

Foundational

Access

Free

Format

Self-paced, text-first modules with formative checks

Who it is for

Anyone using AI tools at work or at home who wants a clear, honest mental model before building habits on top of it.

• Capabilities and limits
• Clear task specification
• Claim verification
• Confidential-data protection

1. M1

   How AI helps and fails

   Three lessons — machine learning vs rules, generative AI and language models, and image/audio/multimodal systems — building the plain-language vocabulary for what modern AI can and can't do, before this course's later modules on task-writing, verification and data protection.

   1. Machine learning vs rulesLesson 1 of 3 · 15–20 minutes
   2. Generative AI and language modelsLesson 2 of 3 · 15–20 minutes
   3. Image, audio and multimodal systemsLesson 3 of 3 · 15–20 minutes
2. M2

   Choosing the right task

   Matching task types — drafting, summarising, classification, analysis — to appropriate AI use patterns, including recognising the non-goals where AI should not be used.

   1. Drafting and summarizing textLesson 1 of 3 · 15–20 minutes
   2. Classification and analysisLesson 2 of 3 · 15–20 minutes
   3. Choosing when not to use AILesson 3 of 3 · 15–20 minutes
3. M3

   Writing better instructions

   Three lessons — goal, context and constraints; examples and output format; iterating on instructions and adding a review criterion — turning vague intent into a checkable, testable task brief an AI system can actually satisfy.

   1. Instructions as task design: goal, context and constraintsLesson 1 of 3 · 15–20 minutes
   2. Examples and output format: making instructions checkableLesson 2 of 3 · 15–20 minutes
   3. Iterating on instructions: when more context beats cleverer wordingLesson 3 of 3 · 15–20 minutes
4. M4

   Data and confidentiality

   Handling sensitive inputs: retention, sharing, redaction and tool choice — designing workflows that keep private data private.

   1. Classifying sensitive inputsLesson 1 of 3 · 15–20 minutes
   2. Retention and sharing: where your input goes after you send itLesson 2 of 3 · 15–20 minutes
   3. Redaction and tool choice: building a safe-use checklistLesson 3 of 3 · 15–20 minutes
5. M5

   Verification workflow

   Three lessons — primary sources and the source ladder, citation quality, and confidence closing with review logs and this course's reusable workflow checklist — building the habit of checking an AI's claims against a real source and writing down what was found before relying on the output.

   1. Primary sources and the source ladderLesson 1 of 3 · 15–20 minutes
   2. Citation quality: does the source actually say thatLesson 2 of 3 · 15–20 minutes
   3. Confidence, verification logs and this course's reusable checklistLesson 3 of 3 · 15–20 minutes