This isn't a list of templates. It's a mental operating system for working with AI — teaching you to think, structure, and reason from first principles.
They treat prompting as clever wording. Real prompt engineering is about intent design, constraint systems, and cognitive scaffolding.
Before you can prompt effectively, you must understand how language models actually "think" — and why that's fundamentally different from human cognition.
LLMs don't understand — they predict. Each word is a probability choice based on all previous context.
Interactive: Click tokens to see prediction flow
The model has no persistent memory between conversations. Everything it "knows" must be in the current context.
Every effective prompt contains five core components. Understanding them lets you diagnose failures and construct solutions.
Toggle components to see effect:
Missing constraints and format specifications may lead to verbose, unstructured output.
The most common prompt failure: mixing these three layers. Learn to separate them for predictable results.
"Summarize this email about the Q3 budget meeting where Sarah mentioned we need to cut costs by 15%"
"TASK: Summarize
CONTEXT: Q3 budget meeting
DATA: [email content]"
What you want the model to DO
Background the model needs to KNOW
Raw material to PROCESS
When prompts fail, they fail in predictable ways. Learn to recognize patterns and fix them systematically.
Toggle errors to inject into prompt:
This prompt should work, but lacks specificity. Outputs may vary significantly between runs.
Four foundational patterns form the basis of almost all effective prompts. Know when to use each.
Best for simple, well-defined tasks where the model's training covers the domain. Fast but may lack nuance.
Fast, simple, low token cost
Less control, variable output
Complex tasks require multiple prompts working together. Design systems, not single prompts.
Pull key information from raw input
Process extracted data with logic
Combine insights into output
Check output against criteria
Each step should have a clear input and output. The model shouldn't need to remember previous steps — you pass the result forward explicitly.
The difference between amateur and professional prompts: precise control over output characteristics.
Exact word counts, specific formats, required sections
Tone suggestions, style preferences, general guidance
The same prompt behaves differently across models. Learn each model's "personality" for optimal results.
GPT-4 excels at following explicit, numbered instructions. Be direct and specific. Avoid conversational language in the prompt.
Professional contexts require reliability, not creativity. Build prompts that produce consistent, defensible output.
Never let AI make final decisions on critical matters. Use it for drafts, analysis, and options — but humans verify and approve.
Getting original output from AI requires strategic constraint. Paradoxically, more boundaries often yield more creativity.
"Write a creative story about space"
"Write 200 words about a janitor on a space station who discovers a hidden message. Use only dialogue. Set during their lunch break."
With power comes responsibility. Every prompt you write has potential consequences beyond the immediate output.
AI inherits and can amplify training biases
Over-reliance can erode human judgment
When should AI assistance be disclosed?
Not templates to copy — examples to learn from. Each prompt demonstrates principles from this masterclass.
Multi-step prompt for evaluating business decisions with pros, cons, and risk assessment.
Constraint system that forces novel combinations and avoids common tropes.
Structured prompt for pulling patterns and anomalies from datasets.
Transform meeting notes into actionable summaries with clear ownership.
You've completed the foundational journey. You now think about prompts as systems, not sentences.