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01 The six units
What you'll cover
01
Foundations
How LLMs actually read a prompt — and why prompt design changes the results you get.
02
Structured prompting
The CO-STAR and CLEAR frameworks, with live before/after "prompt makeovers".
03
Reasoning techniques
Chain-of-Thought, Tree-of-Thought, few-shot prompting, and self-review for better answers.
04
Grounding with data (RAG)
Make AI answer from your documents and data — not guesswork or hallucination.
05
Build & automate
Assemble and run your own AI agent end-to-end in n8n — completely no-code.
02 The capstone
Design it, build it, keep it
The course builds toward one outcome: a multi-stage AI agent, assembled no-code on a visual canvas.
No code required.
No code required
Wired together on a visual canvas
You assemble the agent node by node in n8n — no programming — then run it end to end and watch each stage do its job
01
Design
Frame & ground it
Frame the task, choose the reasoning technique for each stage, and ground it in real data with RAG.
02
Build
Assemble & run it
Wire the stages together on the no-code canvas and run the whole agent end to end.
03
Verify
Check & keep it
Add a self-review and human-in-the-loop check so every output is auditable — then keep the workflow.
03 Practised with
Frameworks & tools
Techniques
-CO-STAR & CLEAR frameworks -Chain-of-Thought reasoning -Tree-of-Thought -Few-shot prompting
-RAG (Retrieval-Augmented Generation) -ReAct-style tool use -Multi-agent design
Tools (free tiers)
-ChatGPT / Claude -Custom GPTs -n8n (no-code automation) -Free data & delivery tools
04 Assessment
How you're certified
Assessment
A 2-hour practical and written assessment. On meeting the competency criteria you receive a WSQ Statement of Attainment. Class ratio 1:20 teaching, 1:1 assessment.
Entry requirements
Completion of secondary education and English literacy at WSQ Level 4. No prior AI or coding experience required.
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