Generative AI & AI Productivity
Learn to work with generative AI the way employers now assume you can — not as a novelty, but as daily working practice. Build prompts that hold up under pressure, design multi-step workflows, automate the repetitive parts of professional work, and know when the output cannot be trusted. Ends with a portfolio of automated workflows you can demonstrate in an interview.
Enrolment is through our partner colleges. You will need the code your college issued.
- Course
- AI-001
- Topic
- Artificial Intelligence
- Modules
- 15
- Levels
- Foundation → Applied → Advanced
How this is shaped for your students
No two cohorts arrive at this course from the same place. Some students already use these tools daily without ever having been taught how; others have barely opened one. Before we confirm the programme we look at the degree and year, how the group is currently using AI, the cohort size, the roles and industries students are aiming at, the hiring market where they will look for work, and the hours the college can give us. We then set the level, choose which of the three stages to run, and rebuild every example and exercise around the work your students will actually be doing.
Who this is for
- Strong fit
- all streams. This is the one course in the library with universal relevance, and it is the natural first purchase for any cohort
- Before you start
- None beyond basic computer literacy
What the course covers
Foundation5 modules
- 01How generative models actually worktokens, context windows, temperature, why the same prompt gives different answers, hallucination as a structural property rather than a bug, the current model families and what each is good at
- 02Prompt constructionrole, task, context, constraints, format; specificity over politeness; few-shot examples; why long prompts beat clever prompts; common failure patterns
- 03Working with documents and datauploading and querying files, summarisation that preserves meaning, extraction into structured formats, the limits of long-context reading
- 04Everyday professional tasksdrafting, editing, rewriting for audience, translation and Indian-English register, meeting notes, research scaffolding, study and revision use
- 05First verification habitschecking a claim, spotting a fabricated citation, the "confidently wrong" pattern, what you must never delegate
Applied5 modules
- 01Prompt engineering as a disciplinechain-of-thought and when it helps, decomposition, structured output (JSON, tables, schemas), system prompts, reusable prompt templates, versioning a prompt
- 02Multi-step workflowschaining outputs, human checkpoints, handling partial failure, designing for a colleague to run it rather than only you
- 03Retrieval and groundingwhy grounding in your own documents changes reliability, building a working knowledge base, citation discipline, what retrieval still gets wrong
- 04AI in the professional toolchainCopilot in Microsoft 365, Gemini in Workspace, AI inside the tools the student already uses; where the embedded assistant beats the chat window and where it does not
- 05Cost, privacy and organisational realitywhat may not be pasted into a public model, data handling under the DPDP Act 2023, free versus paid tiers, why the tool your employer sanctions is the one that matters
Advanced5 modules
- 01Workflow automation at scaleorchestrating AI across systems, batch processing, scheduled runs, error handling, deciding what should never be automated
- 02Evaluation and verification (the differentiating module)designing an evaluation set for your own use case, measuring output quality rather than sensing it, detecting the nearly-right answer, red-teaming your own prompts
- 03Custom assistants and small agentsconfiguring a task-specific assistant, giving it tools and boundaries, testing it against adversarial input, knowing when the agent is the wrong architecture
- 04AI adoption inside a teamwriting usage guidance, training colleagues, measuring whether it actually saved time, handling the colleague who over-trusts it
- 05Judgement and accountabilitywhat remains human, disclosure norms, academic and professional integrity, defending an AI-assisted deliverable to someone sceptical
Three levels, one continuous path
Foundation
You start from working vocabulary and first-hand contact with the tools. If you have never opened them before, you can start here. The work is guided, and you are assessed on knowing the material and applying it simply.
Applied
You turn knowledge into a way of working. You are given a brief with real constraints and incomplete information, you make decisions, and you explain why you made them. This is the level at which most students become useful on an internship.
Advanced
You handle the messy version: conflicting requirements, scale, cost, things going wrong, other people’s opinions, and knowing when not to do the obvious thing. The work is portfolio quality and you have to defend it.
Work you can show an employer
1Foundation
rebuild one recurring task the student actually does into a documented, repeatable prompt, with before-and-after timing.
2Applied
a three-stage workflow for a real function (recruitment screening, campaign drafting, report generation) with checkpoints and a written runbook.
3Advanced
an automated workflow across at least two systems, with an evaluation set and measured quality.
Capstone
AI Adoption Brief for a Real Function. The student picks one team function — a placement cell, a small retail business, a college department — maps its recurring work, identifies where AI genuinely changes it, builds and evaluates two working workflows, writes usage guidance including what must not be automated, and defends the whole thing in a viva. Deliverables: process map, working workflows, evaluation results, usage policy, ten-minute presentation.
Tools you will use
Essential
- ChatGPT
- Claude
- Gemini
- Microsoft Copilot
Recommended
- NotebookLM
- Perplexity
- Google AI Studio
- a spreadsheet-embedded AI function
Advanced
- a workflow automation platform (n8n
- Make or Zapier)
- a lightweight evaluation harness
- an API playground
Jobs this points at
Roles this course is relevant to
AI Productivity Associate · AI Operations Executive · Business Support Analyst (AI-enabled) · Content and Research Associate · any entry role where AI fluency is now assumed rather than differentiating.
What you can do when you finish
- Design and version reusable prompts for recurring professional tasks
- build a multistep AI workflow with human checkpoints and hand it to a colleague
- evaluate AI output against a defined quality standard rather than by impression
- configure a task-specific assistant with appropriate boundaries
- state clearly what they did and did not delegate to a model
Length and delivery
| Option | Typically used for | What it covers |
|---|---|---|
| 30 hours | Value Added Course, or a single-semester elective | All of Foundation |
| 45 hours | Skill Enhancement Course | Foundation, plus selected Applied modules |
| 60 hours | Standard cohort | Foundation, Applied, and the Applied project |
| 90 hours | Two semesters, or intensive | All three levels, plus the Advanced project |
| 120 hours | Full pathway | All three levels, capstone, and placement preparation |
The final option is agreed with your college during programme design.
Bring this course to your college
Enrolment is through our partner colleges. You will need the code your college issued.