Generative AI Primer
What generative AI is, what it can do, and where the lines are
About this course
A short primer for people who need to understand generative AI properly before they make decisions about it. It starts with where AI came from and how analytics matures inside an organisation, then covers what generative AI actually is, what large language models do, and what each modality — text, image, video, audio — is and is not good for.
It closes on ethics, bias, responsible use and the legal and compliance questions that arrive with the technology rather than after it.
Aimed at a mixed business and technical audience. It is conceptual with guided hands-on time on a general-purpose AI assistant, not a build course.
What you’ll be able to do
- Place generative AI correctly against descriptive, diagnostic and predictive analytics, and against machine learning, deep learning and NLP
- Describe in plain language what a large language model is and how it differs from the analytics your organisation already runs
- Say what each generative modality is suited to, and name a task in your own work for each
- Recognise the common failure modes — fabrication, bias, stale knowledge, confident wrongness — and what they mean for a decision you would base on the output
- State the ethical, legal and compliance questions your organisation must answer before deploying generative AI, and who owns each
Before you start
What this course assumes you can already do.
- No technical background needed and no coding in this course.
- Helpful: some exposure to how your own organisation currently uses reporting or analytics.
The curriculum
Open any module to see its objective, every teaching topic, the practice brief and the checkpoint.
Recaps and reference material are included in the LMS.
PART 1Where this comes from
01Overview of Traditional AI
Be able to place generative AI on the same map as the analytics your organisation already does, so it is understood as the next step in a progression rather than as an unrelated novelty.
- Evolution of AIConcept
- The analytics maturity modelConcept
- Descriptive, diagnostic and predictive analyticsConcept
Place your own organisation on the maturity map
Take three reports, dashboards or decisions that already exist where you work. Classify each as descriptive, diagnostic, predictive or prescriptive, and say honestly which one your organisation actually operates at today.
Knowledge checkpoint with explained answers
PART 2What generative AI is
02Introduction to Gen AI
Understand what generative AI is, what a large language model does, and what each modality can realistically be asked for — with enough hands-on time on a general assistant to form your own judgement rather than borrowing one.
- What is generative AI?Concept
- The evolution of generative AIConcept
- Applications of generative AIConcept
- An overview of large language modelsConcept
- Deep learning architectures and AI assistantsHands-on
- Generative AI across modalitiesDemonstration
Run one real task through an assistant and grade it yourself
Pick a task from your own week that you can grade because you already know the right answer. Run it through a general-purpose AI assistant three times with different framing, then grade each output against what you know.
Knowledge checkpoint with explained answers
PART 3Ethics and responsibility
03Introduction to Ethics and Responsibilities in GenAI
Leave able to name the specific ethical and legal questions your organisation has to answer before it deploys generative AI, and who inside the organisation owns each one.
- Ethical challenges, bias and responsible useConcept
- Legal and compliance considerationsConcept
Write the five questions your organisation must answer first
Draft the pre-deployment question set for one generative AI use case in your own organisation. The output should be something a compliance or legal colleague could actually respond to.
Knowledge checkpoint with explained answers
A generative AI readiness brief for your own team
A short written brief, not a build. The deliverable is the document a manager could take to a leadership meeting: what generative AI could do for this team, what it must not touch yet, and what has to be true first.
Where the team is now — Establish the honest starting point before proposing anything.
What you actually tested — Replace opinion about the tools with your own evidence.
What must be true first — The conditions, not the enthusiasm.
The brief — One document, written for someone who will not read the appendix.
Next course
Tell us about yourself or your team and we’ll let you know how and when you can take this course.