How do you plan an AI application before writing code?

How do you plan an AI application before writing code?

An AI project starts with a useful outcome for a user. Before choosing models or generating code, you should be able to explain who the application helps, what they provide, and what they receive when it works.

The Project Introduction episode of Namaste AI introduces the project's vision, objectives, and core functionality. This companion guide gives you a practical framework for thinking through those decisions. We will use a simplified video-creation application as an example, connecting to the season's audio and video generation topics.

Start with one clear user outcome

“Build an AI app” describes a technology choice, but leaves the product undefined. A more useful starting point is: “Help a creator turn a topic into a short narrated video that they can review and download.”

That sentence identifies a user, an input, and an output. It also creates questions worth answering before implementation: How long should the video be? Can the creator edit the script? What does a successful result need to include?

Write down your assumptions. For an initial practice version, you might choose one language, one video format, and a review step before generating the final file. These are example scope decisions, not requirements for every AI application.

Map the journey from input to result

Describe the experience in a few steps:

1. The creator enters a topic and a short brief.
2. The application generates a draft script.
3. The creator reviews or edits the script.
4. The application prepares narration and assembles a video.
5. The creator previews and downloads the result.

Now consider what the creator sees between those steps. A generation request may take time or fail. The interface needs clear progress, a useful error message, and a way to recover. A working journey includes these states as well as the successful result.

Separate AI generation from application logic

The model handles tasks such as drafting text or generating speech. The surrounding application manages forms, permissions, saved projects, file storage, and job status. Video assembly may use conventional media-processing tools; every stage does not need a generative model.

There are also two different uses of AI to recognize. An AI coding assistant can help you build the application. An AI capability inside the product helps the end user accomplish a task. Using a coding assistant does not automatically make the resulting product an AI application.

Keep business rules explicit in your code. For example, deciding whether a user owns a project should come from authenticated identity and stored permissions, rather than a model's judgment.

Think of generation as a sequence of stages

For our example, a possible sequence is: brief, script, reviewed script, narration, video assembly, and final file. Identify what each stage consumes and produces. This makes it easier to locate a failure and decide what can be reused when a later step needs another attempt.

Long-running work can run in a background worker while the interface displays saved job status. Google's introduction to asynchronous tasks explains this separation between a request and background processing. It also explains why retryable work must tolerate repeated execution. This is background reading on the pattern, not a requirement to select that service for your project.

Define a small first version you can verify

Start with one complete journey: submit a brief, review a script, generate a video, and retrieve the saved result. Additional formats, scheduling, and social publishing can be separate milestones.

Write acceptance checks in terms of observable behavior:

• Empty input produces a helpful validation message.
• A submitted request appears as a saved project with a status.
• The finished video contains the reviewed script's narration.
• Reloading the page still allows the creator to find the result.
• A failed generation shows an error and offers an appropriate next step.

Technical completion and content quality are separate checks. A playable video can still contain incorrect claims, poor pronunciation, or mismatched visuals. Review representative outputs for both usefulness and correctness.

Try this before the next episode

Write a short project brief containing the target user, the problem, the input, the expected output, and three essential features. Add one failure scenario and a concrete check for success. Use that brief as context when asking an AI assistant to help plan the work, then review its suggestions against your original goal.