Prompt Templates in Your Codebase
Store, fill, version and test prompts as proper files instead of long strings scattered through your code.
A taste of a lesson
Is it fine to build my prompt with an f-string and drop the user's message straight in?
Inserting the user's message as a value is fine, and an f-string does that safely in Python. Two improvements, though. First, move the fixed text into a template file so it can be reviewed and versioned. Second, wrap the user's message in clear tags, such as and , and tell the model to treat the tagged text as content, not instructions. Never do the reverse, treating user text as a template. Where does your prompt text live today?
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
- Move prompts into files with a simple loader
- Fill templates safely and keep user content delimited
- Store few shot examples as data and render them
- Version prompts and log the version with every call
- Catch accidental prompt changes with snapshot tests
Lesson plan
- 1 Why prompts deserve files See the problems inline prompt strings cause and move one prompt into a file. Start
- 2 Filling templates safely Insert variables into templates without breaking them or mixing in instructions. Start
- 3 Plain formatting versus template engines Choose the simplest templating approach that fits each prompt. Start
- 4 Examples as data Store few shot examples separately and render them into prompts. Start
- 5 Versions and traceability Version prompts and link every model call to the exact prompt used. Start
- 6 Testing templates Add snapshot tests that reveal accidental changes to rendered prompts. Start
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About this tutor
For beginners building apps on model APIs whose prompts have become long strings glued together across several files. You learn to keep prompts in their own files, fill them with variables safely, keep user content clearly delimited, store few shot examples as data, version prompts so every logged call says which version produced it, and catch accidental changes with simple tests. The tutor compares plain string formatting with template engines and explains where each fits. You finish with a small prompt folder and loader you can reuse in any project.
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About the teacher
Teaches developers and product teams to make their first LLM API calls and design simple apps around them
9 tutors 337 lessons taught Sample
I help people go from having used a chatbot to having an app that calls a model. I built web products for a long time and moved into LLM features when they started appearing in every roadmap, so my lessons focus on the decisions that matter in a first build: how a request is shaped, how a conversation is stored,...
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