Build a Full-Stack Databricks App in Five Prompts

Prompt-driven development can compress the distance between an idea and a working application. The interesting question is not whether an agent can generate a page. It is how to structure the work so each prompt produces something testable.

I built a full-stack Databricks application through five scoped prompts: understand, scaffold, connect, refine, and validate.

One prompt, one contract

A useful prompt defines the outcome, boundaries, and the evidence that proves completion.

Add the case detail view.

Use the existing API client and design tokens.
Handle loading, empty, success, and error states.
Do not add a new state library.
Run the relevant tests and report what changed.

This works better than a long description of the final application because each change has a reviewable surface.

Let the repository carry context

The most reliable context lives close to the code: conventions, commands, architecture notes, and examples of established patterns. A coding agent should not have to infer the same rule in every session.

The prompts become smaller as the repository becomes clearer.

The fifth prompt matters most

Generation creates momentum. Validation creates confidence. The final prompt should ask the agent to inspect the complete change, run the build, test the important path, and explain any remaining uncertainty.

Five prompts is not a magic number. The principle is to turn a large build into a sequence of small contracts, each with its own evidence.