Effective prompt writing
Think not about what AI gives you, but about what you tell it. In this module we first understand how the model sees the world (tokens and context window), then master the single variable that determines output quality: the instruction.
Same model, same tool, different result.
Two different instructions on the same topic. The difference isn't ability, it's specificity.
Result: encyclopaedic language, disconnected from the class level, a text with no specified duration or format. Reads like a quote from any textbook.
Result: an explanation in Year 8 vocabulary, in a spoken rhythm, bridging from concrete to abstract and targeting misconceptions from the outset.
Token: the unit of the model's world.
The model doesn't read sentences the way you do; it breaks them into meaningful fragments. These fragments are called tokens. Click the words to see how they're broken up.
Words are broken into sub-word pieces. The model processes these tokens rather than whole words, which is why longer technical words cost more tokens. Being fluent in your prompts makes them both cheaper and clearer.
How many tokens is your sentence?
Those two words were prepared examples. Now write or paste your own prompt: it gets broken into tokens so you can see the cost with your own eyes. The split is approximate, real models break slightly differently, but the order of magnitude is right.
Context window: the model's working memory.
The number of tokens the model can hold in mind at the same time. It grew a hundredfold in three years, for a teacher, this means "being able to place all the relevant documents in front of it".
Teacher translation: "I'm placing this learning objectives list, this unit plan, last week's test, and 3 book chapters in front of you. Evaluate all of them together and design a lesson for this week."
The model is like a workdesk.
The best analogy you can think of: a teacher's desk. Hover over the cards.
A large window sees a lot. But keeping the desk tidy is still the teacher's job.
Five components, one checklist.
You don't always need to use all five. Think of this as a list to come back to when you're stuck.
No single framework, but they all say the same thing.
Madlen's five-component framework isn't the only right answer. There are two other short recipes teachers frequently use around the world. All share the same message: tell the model who you are, what you want, and for whom/how to produce it.
The list you just saw. Use it like a checklist, you don't always need all five.
Persona (role), objective (task), parameters (audience, tone, reading level). Easy to remember in three steps.
Works well for research tasks: ask for a "Source" (and verify it's real), then "Refine", meaning improve iteratively.
Build your own prompt with five components.
Make a choice for each component; your prompt takes shape live below. When done, copy it, try it in Madlen Assistant, then start adapting it to your own lesson.
- (leave empty)
- You are a Year 9 maths teacher.
- You are a secondary physics teacher who values connecting lessons to everyday life.
- You are a primary school teacher with students who have additional learning needs.
- You are a Year 6 teacher preparing students for transition to secondary school.
- (leave empty)
- Students should be able to convert between fractions and decimals.
- Students should be able to explain the three main stages of photosynthesis in their own words.
- Students should be able to compare two sources and identify different perspectives.
- (leave empty)
- Mixed-ability class of 30 Year 7 students, 40-minute lesson.
- No devices available, only whiteboard, pen and paper.
- Inclusive classroom with a student who has a hearing impairment, verbal-only delivery.
- (leave empty)
- Give the output as a 3-column table: concept, definition, everyday example.
- Include an answer key in brackets under each question.
- 5 bullet points, each no longer than 2 sentences.
- (leave empty)
- Do not use mathematical symbols; write everything in words.
- Keep it under 200 words.
- Use only examples familiar to UK students.
Fix the bad prompt.
The most popular part of the workshop. Which pieces of information should be added to the weak prompt below? Select everything that's missing, then check.
How many components does your prompt carry?
Repairing a prepared prompt is easy; auditing your own is hard. Write a prompt you would genuinely use below and it gets scanned against the five components. The scan works at word level, so it is a checklist rather than a grade, but it catches a missing component surprisingly well.
Nine prompt tools, three levels.
Nine techniques to strengthen your prompt once you've built the five-component framework. Pick the level you're ready for, copy the real classroom example under each tool, and try it in Madlen Assistant. Start at the beginning, progress as you master each one.
Same task, from mediocre to excellent in three rounds.
The first prompt is rarely the last. Watch how we refine a seating plan over three rounds. Track what gets added to the prompt in each round and how the output improves.
Three more techniques.
1 Teach with examples ›
2 Ask it to think ›
3 Iterative refinement ›
Five common mistakes.
1 Writing too generally ›
2 Giving contradictory instructions ›
3 Trusting blindly ›
4 Not specifying your context ›
5 Settling for the first output ›
Watch what's hidden in your prompts.
AI companies need vast amounts of real human data to train models. Every prompt you write could potentially become training data somewhere. Any information from your classroom, your students or parents that enters a prompt is no longer entirely under your control.
Not "Ahmed's latest test" but "a Year 7 student's test". Don't write real names, ID numbers, or class identifiers. If you're uploading a test paper, cut out identifying information.
Look for a setting like "don't use this conversation for training". It exists in most tools but is on by default.
The simplest and most powerful rule. Don't share a parent's message verbatim; anonymise the subject and consult.
The pedagogical and ethical messages of the workshop require the same attention: write a specific prompt, it improves output; don't give specific personal data, it protects privacy.
Fill in the blanks.
- token pool
- context window
- training data
- losing internet connection
- exceeding the limit
- the model getting tired
- ask for it explicitly
- hint at it
- never mention it
Why is the prompt "write a short but detailed summary" weak?
Module 2 complete.
You now know how the model sees the world and how to talk to it. In the final module, you'll use this skill to produce a real learning objective from start to finish and assess it two different ways.
Module 3: Materials & assessment →