Prompting & Context for Apps
Prompts that hold up in production, not just in a chat window
Prompting for software is different from prompting in a chat app: your prompt runs thousands of times on inputs you never saw. Learn to write, template, chain, version, and debug prompts as product code.
- Lessons
- 9
- Reading time
- 96 min
- Level
- Beginner
- Access
- Full course
Lessons in this module
- 2.110 min
Prompts Are Product Code
Why a prompt inside an app needs the same care as code — ownership, review, tests, and versioning — and how that changes the way you write it.
- 2.212 min
Writing System Prompts for Product Features
A repeatable structure for feature-level system prompts: role, task, inputs, rules, output format, and edge cases — built up on a real feature.
- 2.311 min
Few-Shot Examples: Showing Instead of Telling
How to choose, format, and maintain examples that teach a model your exact output style without overfitting it to the examples themselves.
- 2.410 min
Prompt Templates, Delimiters, and Safe Interpolation
Build prompts from typed templates, wrap user data in clear delimiters, and keep untrusted input from being read as instructions.
- 2.511 min
Breaking Work into Prompt Chains
Split a complex task into small, checkable steps — with validation gates between them — and know when one bigger call is the better choice.
- 2.612 min
Context Assembly: Deciding What the Model Sees
Selecting, ordering, and trimming the user data, documents, and history that go into each call — the hidden skill behind features that feel smart.
- 2.711 min
Managing Conversation History and State
Store, replay, trim, and summarize multi-turn conversations; keep app state out of the transcript; and avoid the bugs that come from editing history.
- 2.89 min
Versioning and Managing Prompts
Where prompts should live, how to version and review them, when a prompt management tool helps, and how to roll back a bad change fast.
- 2.910 min
Debugging Prompts Systematically
A method for fixing a prompt that misbehaves: reproduce, isolate, form a hypothesis, change one thing, and confirm on a test set — not on vibes.
About this module
Prompt engineering for apps is a different discipline from prompting in a chat window. When you chat with a model, you read every answer and rephrase when it misses. A prompt inside a product runs thousands of times a day on tickets, documents, and messages nobody reviews, and its output goes straight into your interface, your database, or the next step of a pipeline. The prompt stops being a message and becomes product logic written in plain language.
That shift is why so many AI features work in the demo and fail quietly in production. A system prompt that handles ten test inputs meets an automated out-of-office reply, a ticket in another language, or a customer who types a closing tag. A field that is null renders as the word null. A one-sentence edit made to fix one bug changes behavior for every customer, and nothing fails until the complaints arrive.
This module teaches the habits that make prompts dependable. You will treat prompts as versioned, owned, tested code; write feature-level system prompts with a clear structure of role, task, inputs, rules, output format, and edge cases; use few-shot examples without letting the model copy them; build typed templates that escape and bound untrusted input; and split complex jobs into prompt chains with validation gates between steps, while recognizing when one call is the better design.
The second half of the module covers everything around the prompt: assembling the right context for each call within a token budget, storing and trimming multi-turn conversation history, versioning and rolling back prompt changes, and a systematic method for debugging a prompt that misbehaves. Every lesson uses TypeScript examples built on a realistic help-desk app, so you finish with patterns you can apply directly to the AI features you ship.
Questions about Prompting & Context for Apps
How is prompting for an app different from prompting ChatGPT?
In a chat, you see each answer and can rephrase immediately. In an app, the same prompt runs on thousands of inputs you never see, and its output feeds code or a UI directly. That means the prompt needs a precise specification, handling for edge cases, safe treatment of user input, and tests, just like any other code.
What should a system prompt for a product feature include?
A useful structure has six parts: the role and who reads the output, the task and what good looks like, a description of each input and how far to trust it, rules with their reasons, the output format, and known edge cases. Per-request data, secrets, and permission checks belong elsewhere, in the user message or in your code.
How many few-shot examples should I use?
Three to five well-chosen examples is a good starting point for most formatting and style tasks. Choose them for variety and include hard cases, then measure on a test set whether more examples actually help. Start without examples first, since current models follow written instructions well.
Do XML tags or delimiters prevent prompt injection?
They help the model tell your instructions from user data, and escaping stops users from forging your tags, but they do not make injection impossible. The reliable defense is designing the feature so that an injected instruction cannot do much damage, for example by keeping a human review step and not giving the model tools it does not need.
When should I use a prompt chain instead of a single prompt?
Use a chain when a task has separable parts with clear outputs, when a cheap check between steps can stop bad work early, or when steps need different models or should see different data. Prefer a single call when a user is waiting on the result or when the parts need each other's full context. Test both designs on the same cases before deciding.
Should prompts be stored in code or in a database?
For most teams, prompts belong in the codebase next to the code that calls them, versioned and reviewed like any other change. A prompt management tool can help when non-engineers edit prompts often or when you need to change prompts without a deploy, as long as every change still gets reviewed and tested.