Prompting Across Models: Adapting When You Switch
Keep your prompts working when you change assistants, models or when a model is updated.
A taste of a lesson
We moved our team to a different assistant and our summary prompt now produces much longer answers. Should we rewrite everything?
Probably not. Length differences are one of the most common changes between models, and usually a small fix works. Add an explicit limit near the end, such as 'five bullets, under 80 words total', and say what to leave out. Then rerun the prompt on ten past inputs and compare with your old outputs using a short checklist. Only rewrite further if other checks fail. Do you have a set of past inputs and outputs we can use as a baseline?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Write prompts that rely on clear structure instead of model specific tricks
- Run a fair, blind comparison of models on your own tasks
- Adjust prompts for differences in verbosity, format and caution
- Detect regressions after a model update with a fixed test set
Lesson plan
- 1 Why the same prompt behaves differently Understand the main ways models differ in following prompts. Start
- 2 Writing portable prompts Build prompts that work well across models. Start
- 3 Designing a fair comparison Compare models on your real tasks with a repeatable method. Start
- 4 Adapting without starting over Make small, targeted adjustments when moving a prompt to a new model. Start
- 5 Beyond output quality Include practical factors in a team decision about assistants. Start
- 6 Watching for regressions Catch changes in behaviour after updates before they cause problems. Start
Try asking
About this tutor
Prompts that work well on one model can behave differently on another, and even the same product can change behaviour after an update. Teams that rely on shared prompts feel this most. This tutor teaches how to write prompts that travel well, how to compare models fairly on your own tasks, how to spot differences in verbosity, formatting, instruction following and caution, and how to adapt without starting over. It is for advanced users, team leads and anyone who chooses between assistants for their organisation and wants a method rather than a ranking that goes stale in weeks.
Reviews
4.5
2 ratingsSample
- Sebastian R.Sample
We caught a regression after an update because we had kept the test set from this course. Saved a messy week.
- Ingrid N.Sample
Refreshingly neutral. The blind comparison sheet helped our team choose without arguing about brands.
About the teacher
Prompt workflows for heavy users: chaining, standing instructions, long documents and reasoning models
9 tutors 269 lessons taught Sample
I work with people who already use AI assistants every day and want more dependable results. My background is in operations and process design, which taught me to treat a prompt like a small procedure: inputs, steps, checks and a clear output. I teach chaining, reusable instructions, long document work and how to test whether a prompt change actually helped....
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