Prompt Engineering & Applied Generative AI

An original Mai Studies programme covering large language model fundamentals, structured prompt design, retrieval-augmented generation, agentic workflows, evaluation and responsible AI adoption. Content is written by Mai Studies and references no third-party course materials.

6 weeks 36 guided hours Beginner

Join membership to study

Included with Mai Studies membership at no extra cost. Fee waivers are available through scholarships and open access.

Prompt Engineering & Applied Generative AI course artwork

What you will achieve

Explain how large language models generate and rank responses
Design reusable prompt templates with roles, context and constraints
Build retrieval-augmented generation flows over your own documents
Plan an agentic workflow with tools, guardrails and human review
Evaluate output quality and document risks before adoption

Entry guidance

— No prior AI experience required
— Basic familiarity with everyday office software

Original Mai Studies learning material. This is a professional completion programme, not a regulated qualification or university accreditation.

Programme syllabus

4 applied modules

Lessons: 8
Assessed tasks: 4
Practice questions: 48
Timed mock papers: 2

Foundations of Generative AI

How LLMs work, what they can and cannot do, and where they fit in business.

2 lessons · 12 practice questions · 1 assessed task

1. How Large Language Models Work21 min study · 3 learning objectives · practical activity
Tokens, training and prediction in plain language.
2. Where Generative AI Fits in Business21 min study · 3 learning objectives · practical activity
High-value, low-risk use cases to start with.

Foundations of Generative AI: marked task — marked out of 100, pass at 80%

Prompt Design in Practice

Roles, context, constraints, examples and structured outputs.

2 lessons · 12 practice questions · 1 assessed task

1. Anatomy of a Strong Prompt21 min study · 3 learning objectives · practical activity
Role, task, context, format and constraints.
2. Structured Output and Few-Shot Examples21 min study · 3 learning objectives · practical activity
Getting consistent, machine-usable answers.

Prompt Design in Practice: marked task — marked out of 100, pass at 80%

RAG and Agentic Workflows

Grounding models in your documents and planning tool-using agents.

2 lessons · 12 practice questions · 1 assessed task

1. Retrieval-Augmented Generation21 min study · 3 learning objectives · practical activity
Grounding answers in your own documents.
2. Agentic AI Workflows21 min study · 3 learning objectives · practical activity
Tools, planning, guardrails and human checkpoints.

RAG and Agentic Workflows: marked task — marked out of 100, pass at 80%

Evaluation and Responsible Adoption

Measuring quality, managing risk and rolling out AI at work.

2 lessons · 12 practice questions · 1 assessed task

1. Evaluating AI Output21 min study · 3 learning objectives · practical activity
Rubrics, test sets and error budgets.
2. Responsible Adoption21 min study · 3 learning objectives · practical activity
Data protection, transparency and governance.

Evaluation and Responsible Adoption: marked task — marked out of 100, pass at 80%

Timed mock papers

Prompt Engineering & Applied Generative AI: timed mock paper 1 — 180 minutes, 30 objective questions plus written and computational tasks
Prompt Engineering & Applied Generative AI: timed mock paper 2 — 180 minutes, 30 objective questions plus written and computational tasks

Lesson pages, assessed tasks and mock papers open the moment you join this programme. Teaching material is studied online; the notes you write are yours to download.

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