Working professionals · evenings
Pro · Professionals
Full curriculum document
Working professionals upskilling into AI while employed
Contents · 6 units
- 1AI foundations for practitionersPRO-U1 · Month 1 · 40 GLH · Internal portfolio
- 2Prompting & structured output in productionPRO-U2 · Month 2 · 50 GLH · Internal portfolio
- 3Build with LLMs & RAGPRO-U3 · Month 3 · 60 GLH · Internal portfolio
- 4Automation & agents at workPRO-U4 · Month 4 · 55 GLH · Internal portfolio
- 5Evaluation, safety & cost controlPRO-U5 · Month 5 · 50 GLH · Internal portfolio
- 6Ship it responsibly at workPRO-U6 · Month 6 · 55 GLH · Portfolio + exam
Overview & approach
You already solve real problems at work — the fastest way to add AI is to attach it to one of them. Pro starts from a genuine problem in your role and builds the smallest AI capability that measurably improves it.
The first-principles learning loop
- 1Problem
- 2Understanding
- 3Reasoning
- 4Design
- 5Implementation
- 6Testing
- 7Reflection
- 8Improvement
Every unit runs this loop, then goes round again — deeper each time.
Competencies & responsible practice
Core competencies
- Communication & collaboration
- Critical thinking & problem solving
- Creativity & imagination
- Citizenship
- Digital literacy
- Learning to learn
- Self-efficacy
Ethics & responsible practice
- Digital citizenship & online safety
- Data protection & privacy
- AI ethics, bias & fairness
- Environmental sustainability (compute cost)
- Financial literacy & entrepreneurship
- Inclusion & accessibility
- Mental well-being & healthy screen habits
- Career & life skills
The 6 units in full
PRO-U1 · Month 1 · 40 GLH · Internal portfolio
AI foundations for practitioners
Strand: Applied AI literacy
Key inquiry: “For a real task in my job, is AI the right tool — and which shape of AI fits?”
You ship: A capability map for your role: where AI helps, where it doesn’t, and the first target.
LO1 · What AI is good at
- • Match tasks to AI shapes (summarise, extract, classify, draft, answer)
- • Judge where a confident wrong answer is unacceptable
LO2 · Prompting well
- • Design, test and version prompts like real code
- • Get reliable structured output and validate it
Core competencies
- Critical thinking & problem solving
- Digital literacy
- Learning to learn
Ethics & responsible practice
- AI ethics, bias & fairness
- Data protection & privacy
Hands-on practice
- Audit your week for AI-suitable tasks and rank them.
- Build a prompt that reliably produces usable structured output.
- Reflection: where would you keep a human in the loop, and why?
Proficiency (Emerging · Proficient · Expert)
| Emerging | Maps tasks to AI with support. |
|---|---|
| Proficient | Designs reliable prompts independently. |
| Expert | Produces a sharp, defensible capability map for a real role. |
PRO-U2 · Month 2 · 50 GLH · Internal portfolio
Prompting & structured output in production
Strand: Applied prompt engineering
Before this unit: An AI capability map (Unit 1).
Key inquiry: “How do we get reliable, machine-usable output from a model — every time, not just once?”
You ship: A versioned prompt system that produces validated structured output, with a small eval set.
LO1 · Reliable prompting
- • Design, test and version prompts like real code
- • Get structured output and validate it against a schema
LO2 · Evaluation
- • Build a small evaluation set for the task
- • Measure reliability and improve the weak cases
Core competencies
- Critical thinking & problem solving
- Digital literacy
- Self-efficacy
Ethics & responsible practice
- Data protection & privacy
- AI ethics, bias & fairness
Hands-on practice
- Turn a flaky prompt into a reliable, versioned one.
- Validate structured output and handle the failure cases.
- Build a 20-case eval set and track a score over versions.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Writes reliable prompts with support. |
|---|---|
| Proficient | Versions prompts and validates output independently. |
| Expert | Ships a measured prompt system colleagues can reuse. |
PRO-U3 · Month 3 · 60 GLH · Internal portfolio
Build with LLMs & RAG
Strand: Applied generative AI
Before this unit: A reliable prompt system (Unit 2).
Key inquiry: “How do I make a general model answer reliably from my organisation’s own data?”
You ship: A RAG assistant over real work documents, with an evaluation harness.
LO1 · Retrieval
- • Build an embeddings + retrieval pipeline over a real corpus
- • Chunk, index and evaluate retrieval quality
LO2 · Reliability
- • Mitigate hallucination, injection and data-leakage risks
- • Measure answer quality with an evaluation set
Core competencies
- Critical thinking & problem solving
- Digital literacy
- Self-efficacy
Ethics & responsible practice
- Data protection & privacy
- AI ethics, bias & fairness
Hands-on practice
- Ship a RAG assistant over a real (safe) document set.
- Red-team it for leakage and prompt injection.
- Build an eval set and defend your quality numbers.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Builds a working RAG assistant. |
|---|---|
| Proficient | Adds evaluation and handles edge cases independently. |
| Expert | Ships a hardened, evaluated assistant colleagues rely on. |
PRO-U4 · Month 4 · 55 GLH · Internal portfolio
Automation & agents at work
Strand: Applied automation
Before this unit: A working RAG/LLM feature (Unit 3).
Key inquiry: “Where can autonomy safely remove real work — and where must a human stay in control?”
You ship: An automation or agent that removes a recurring task, with guardrails and logging.
LO1 · Tool use & guardrails
- • Give a model tools and orchestrate a multi-step task safely
- • Add checkpoints, retries and human-in-the-loop controls
LO2 · Observability
- • Log, trace and cap the cost of an automation
- • Measure time saved and error rate honestly
Core competencies
- Critical thinking & problem solving
- Self-efficacy
- Communication & collaboration
Ethics & responsible practice
- AI ethics, bias & fairness
- Career & life skills
Hands-on practice
- Automate one recurring task and measure hours saved.
- Add guardrails and a kill switch; test them.
- Instrument cost and errors; set a hard budget.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Automates a task with support. |
|---|---|
| Proficient | Adds guardrails and observability independently. |
| Expert | Ships a safe, measured automation adopted by the team. |
PRO-U5 · Month 5 · 50 GLH · Internal portfolio
Evaluation, safety & cost control
Strand: AI evaluation & operations
Before this unit: A working AI feature or automation (Units 3–4).
Key inquiry: “Before this touches real work, how do we prove it is good enough — and keep it affordable and safe?”
You ship: An evaluation harness plus a cost-and-safety report for your AI feature.
LO1 · Evaluation
- • Define quality metrics and build an evaluation set
- • Catch regressions before they reach real users
LO2 · Safety & cost
- • Red-team for harmful output and data leakage
- • Cap and monitor cost per request
Core competencies
- Critical thinking & problem solving
- Self-efficacy
- Citizenship
Ethics & responsible practice
- AI ethics, bias & fairness
- Environmental sustainability (compute cost)
Hands-on practice
- Build an eval that fails when quality drops.
- Red-team the feature for unsafe or leaked output.
- Set a cost budget and alert when it is exceeded.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Evaluates with support. |
|---|---|
| Proficient | Builds evals and controls cost and safety independently. |
| Expert | Ships an evaluated, safe, cost-bounded feature. |
PRO-U6 · Month 6 · 55 GLH · Portfolio + exam
Ship it responsibly at work
Strand: Deployment & responsible AI
Before this unit: An evaluated AI feature or automation (Units 3–5).
Key inquiry: “What does it take to put this in front of colleagues or customers — safely and to standard?”
You ship: A deployed AI tool with monitoring, a model/product card and an adoption plan.
LO1 · Deploy & monitor
- • Deploy with monitoring, access control and a rollback plan
- • Apply data-protection and security requirements
LO2 · Communicate
- • Document limits, risks and an ethical-use policy
- • Pitch adoption to stakeholders with evidence
Core competencies
- Communication & collaboration
- Citizenship
- Self-efficacy
Ethics & responsible practice
- Data protection & privacy
- AI ethics, bias & fairness
Hands-on practice
- Deploy the tool with monitoring and a rollback plan.
- Write a model/product card covering risks and limits.
- Pitch adoption to your team or manager with the numbers.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Deploys with basic monitoring. |
|---|---|
| Proficient | Adds responsible-AI docs and a validated rollout. |
| Expert | Ships a production tool adopted at work with a strong case. |
What you walk away with
A portfolio of 6 real, shipped artefacts — one per unit, assessed to standard.
- 1 A capability map for your role: where AI helps, where it doesn’t, and the first target.
- 2 A versioned prompt system that produces validated structured output, with a small eval set.
- 3 A RAG assistant over real work documents, with an evaluation harness.
- 4 An automation or agent that removes a recurring task, with guardrails and logging.
- 5 An evaluation harness plus a cost-and-safety report for your AI feature.
- 6 A deployed AI tool with monitoring, a model/product card and an adoption plan.
Proficiency rubric (Emerging → Expert)
| Proficiency level | What the evidence shows |
|---|---|
| Emerging | Completes the work with guidance; the artefact functions for the core case and the practitioner can explain what it does and why. |
| Proficient | Works independently, handles real-world edge cases, evaluates the result, and justifies design decisions from first principles. |
| Expert | Delivers work adopted by real users or partners, reasons explicitly about trade-offs — cost, risk, safety — and extends beyond the brief with original contribution. |