ChatGPT Masterclass
Take Your Skills Further
Go further with ChatGPT: get more helpful answers, take on bigger tasks, work with documents and data, and build routines you can repeat.
What you will be able to do
Included
- 8 modules · 32 short lessons
- 8 module quizzes with explanations
- Downloadable worksheets and workbooks
- Reusable prompt templates you can adapt
- Hands-on projects in every practical module
- Final assessment and a capstone project
Downloadable worksheets, prompt templates and the capstone kit are unlocked with Pro.
Preview · Module 1 — Getting Better Answers
Free previewLearning objectives
- Identify the six parts that separate a professional prompt from a casual one
- Rewrite a business request so the output is usable without a second attempt
- Recognise which parts of a prompt you can safely leave to the model to infer
Why expert prompts look different
A beginner asks for an output. An expert specifies the conditions the output must satisfy. That single shift explains most of the quality gap.
Expert prompts are not longer for the sake of being longer. They contain specific information the model cannot invent: who you are, what constraint you are under, what a good result looks like, and what would make the answer unusable.
- •Role and audience — who is speaking, who reads it
- •Objective — the outcome, not the activity
- •Constraints — limits on length, tone, claims, and risk
- •Context — the facts the model cannot know
- •Format — structure you can use immediately
- •Acceptance criteria — how you will judge the result
The test that matters
If your prompt could be answered usefully for anyone in the world, it is not specific enough. It should only work for your situation.
The acceptance-criteria habit
The most underused element is the last one. Telling the model how the output will be judged often improves it more than any amount of extra context.
Before — a strong beginner prompt
Write a proposal for my consulting services.
After — an expert prompt
Objective: a two-page proposal that gets a decision from a cautious operations director. Context: I am a solo consultant helping mid-sized logistics firms cut warehouse errors. This prospect has had a failed automation project before and is sceptical. Constraints: no jargon, no promises of specific percentages, acknowledge their previous bad experience directly, keep under 700 words. Format: heading, the problem as they experience it, my approach in three phases, what I need from them to start, and the honest risk. I will judge it on: (1) would a sceptical operations director keep reading past paragraph one, (2) is every claim one I can defend in a call, (3) is the next step unambiguous.
The second version is not just longer. Every added line removes a way the output could be wrong for this specific reader.
What to leave out
- •Do not restate general knowledge. Explaining what a proposal is wastes attention.
- •Do not stack contradictory instructions ('be brief but cover everything in depth').
- •Do not ask for three different outcomes in one prompt — split the work into stages.
- •Do not bury the task in the middle of a long message. Task first, background after.
Common mistake
Writing a long prompt in which the actual request appears last. The model has to guess which paragraph is the instruction and which is context. Lead with the ask.
Exercise
- 1.Take a request you sent recently that produced a usable-but-generic answer.
- 2.Write down the missing parts from the six-element list.
- 3.Rewrite it with acceptance criteria included.
- 4.Compare the two answers and note which criteria the model actually honoured.
Prompt to use
Ask me the questions you need to produce [output] at a professional standard, then wait for my answers before you write anything.
Key takeaways
- • Expert prompts specify the conditions the output must meet, not just the output itself.
- • Acceptance criteria improve quality more than extra background information.
- • Ask the model what it needs before it answers; you will find your own gaps faster.
The full masterclass includes 32 lessons across 8 modules, all four preview lessons above included.
Masterclass curriculum
Why some prompts work better than others, and how to write one you can reuse instead of rewriting from scratch.
Final assessment
End-of-course assessment covering every module, with explanations for each answer.
Capstone project
One real project built across the masterclass, with a brief, checkpoints, and a marking rubric.
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