Practical AI references you can use immediately
Plain-English checklists, definitions, and responsible-use guides for learners who want useful habits before, during, and after the courses.
Use the prompting checklist
Explore courses
- Prompt betterA quick checklist for clearer AI instructions.
- Learn the languagePlain-English AI terms without jargon fog.
- Use AI responsiblyWorkplace habits for privacy, bias, and review.
A working checklist before you send a prompt
Clear instructions improve many AI tasks, but prompt quality is only one factor. Before you send a prompt, check the task, context, source material, tool limits, and how the result will be reviewed.
Prompt quality check
Use this as a fast pre-flight review for work prompts, study prompts, and AI-assisted drafts.
- Perspective and audience – state the expertise or point of view needed, and who the output is for.
- Task – give one clear instruction; split compound requests.
- Context – include the relevant background the model cannot see.
- Format – specify table, list, options, length, tone, or structure.
- Example – show one example of good output when quality matters.
- Refine – treat the first answer as a draft and ask for specific changes.
AI terms in plain English
Use these definitions to build shared language before going deeper into AI courses, workplace discussions, or technical material.
Computer systems designed to produce outputs such as predictions, recommendations, decisions, or generated content for tasks that usually require human-defined goals and judgement.
A major approach used to build modern AI systems: models learn patterns from examples instead of relying only on fixed rules written by people.
AI designed to produce content such as text, images, code, or audio by using patterns learned during training.
A generative AI system trained on text, which predicts likely continuations and powers many AI assistants.
The instruction and context you give a generative AI system. It is one important quality lever alongside the model, source material, tools, and human review.
Confident, fluent output that is factually wrong. It is a normal failure mode, and the reason human review matters.
The process of adjusting a model using large amounts of example data so its predictions improve.
Additional training that adapts an existing model to a specific task, style, or domain.
Responsible AI
Everyday guardrails for using AI at work
AI can help with speed and quality, but only when people stay accountable for what is shared, checked, and published.
Responsible-use checklist
- Verify before you rely. Treat factual claims, citations, and numbers as unchecked until confirmed.
- Protect private data. Do not paste confidential, personal, or client information into unapproved tools.
- Keep a human in the loop. AI drafts; a person with accountability decides.
- Watch for bias. Review outputs that affect people with extra care.
- Respect ownership. Understand your organisation’s and tools’ rules on copyright and reuse.
- Disclose where it matters. Be transparent where your audience or organisation expects it.
Use the AI Foundations learner workbook
The complete learner workbook and eight focused course resources are available online and as printable PDFs. Use them with the lessons, checkpoints, and capstone.