Prompting for Features, Logic, and Data
Explore how to create detailed prompts that guide AI to develop app features responsibly by defining their tasks, rules, validation checks, data handling, and verification steps. This lesson helps you understand how precise prompting strengthens feature functionality and reliability in vibe coding workflows.
A screen can look usable and still break down when users try to complete an actual task. A booking form may look complete, but it still needs rules for valid dates, available time slots, reservation storage, and confirmation messages. At this point in vibe coding, the focus moves from the visible interface to the behavior the feature needs to support.
Less effective prompts often only name the feature. Stronger prompts describe what the feature is responsible for, the rules that control its behavior, the validation checks that prevent invalid input or unsafe actions, and the data it saves or updates. Start by defining the job the feature needs to handle.
Prompting for feature responsibility
A feature is a capability the app provides, such as booking an appointment, saving a note, or tracking an expense. When we prompt for a feature, we need to describe the responsibility of that capability in direct language.
Main responsibility
The main responsibility tells the model what the feature exists to do. This should be the central action, not a broad product idea.
Prompt: Create a booking feature that lets us reserve a one hour study room slot. |
This works because the request has one clear job. The model is not being asked to build a full event system. It is being asked to handle study room reservations.
What the feature should produce
The next step is to say what result the feature should create after the action happens. A good feature prompt usually names the output, not only the input.
Prompt: Create a booking feature that lets us reserve a one hour study room slot and then show the saved reservation with room name, date, and time. |
This makes the prompt stronger because the model now knows what useful result should appear after the reservation is made.