4.2 – Adding New Features to Your Prototype Lesson

Lesson Overview

Bringing a new feature to a prototype leverages all the skills you’ve gained in previous chapters. In this lesson, you’ll apply both planning and technical skills by integrating a workout scheduling feature across your application stack—front end, back end, and database. The process starts by configuring your local environment and clearly describing your requirements to the AI assistant. You’ll see how to guide the assistant with context from your codebase, direct it to update or create files, and iterate as issues and bugs arise.

This lesson addresses the common challenge of integrating new, cross-cutting features that require coordination between the UI, server endpoints, and data storage—key for any evolving product. By practicing this workflow, you’ll learn effective strategies to scope requests, handle error messages, and guide your AI tool for reliable changes. These project-based skills are essential for solo builders or teams who need to ship updates quickly without losing control of their codebase. Scheduling workouts, while used here as an example, reflects real-world needs faced in SaaS, internal tools, and consumer apps, making this lesson relevant far beyond just fitness projects.

Who This Is For

Building and integrating new features is a skill every digital product creator needs. This lesson will be especially useful if you:

Are a solo entrepreneur or early-stage founder expanding your MVP.

Work on a product team and need to quickly prototype and release features.

Use AI coding assistants to speed up full stack development.

Maintain applications where database, server logic, and UI all interact.

Are learning through hands-on projects and want confidence deploying live updates.

Need practical exposure to debugging and iterating with AI tools.

Where This Fits in a Workflow

You’ll turn user needs into real product updates with this lesson. For example, once basic workout tracking is set up, adding a scheduling feature lets users plan future sessions directly from the app. The workflow here—planning, prompting, coding, debugging, and deploying—is the same process for any new feature your users request.

In practice, this approach helps teams respond quickly without long manual coding cycles. Instead of addressing only one code layer at a time, you orchestrate changes across the UI, server, and database. This is how continuous improvement happens in SaaS products, learning tools, and marketplace apps: you identify a priority, break it down, use tools to build iteratively, and ship to production—just as you’ll see in this lesson.

Technical & Workflow Benefits

Traditionally, introducing new features means touching multiple files, manually tracing dependencies, and fixing scattered bugs one by one. With an AI coding tool and a clear workflow, you can describe a full stack goal—like “schedule a workout, show it on a calendar, and store it in the database”—and let the assistant do much of the heavy lifting. You can tag relevant files and watch the tool generate or update entire components at once.

This saves significant time: instead of writing boilerplate for UI dialogs, endpoint handlers, and database migrations yourself, you can focus on guiding, reviewing, and correcting the most important details. For example, you’ll spot and fix integration bugs more quickly, validate SQL changes as soon as they’re suggested, and push updates to production with confidence. For anyone who wants to reduce overhead and increase deployment speed, this method streamlines both the build and iteration process—letting you focus on bigger product goals, not just fixing code line by line.

Practice Exercise

Try adding a “schedule workout” feature to your own prototype or sample app:

1. Set your project to local development mode, ensuring API requests point to your local server.
2. Describe your feature in an AI coding tool, emphasizing the need for a UI dialog, selectable workouts, frequency options (one-time or recurring), and database storage for schedules. Tag any files that should be included.
3. As the assistant makes changes, run your app and take note of any errors or unwanted UI behavior. Use the feedback loop by copying error details back into the chat for fixes.

When finished, compare your experience to the workflow shown in the lesson video. What went smoothly, and where did you need to provide extra guidance or troubleshoot?

Course Context Recap

This lesson builds directly on the fundamentals established earlier in the course—where you learned to prompt AI tools, handle local environments, and deploy working prototypes. Now, you’re layering on the experience of shipping a full stack feature, resolving cross-cutting issues, and updating a live web application. After completing this, you’ll be ready to tackle more advanced improvements, from tracking user results to enriching the product roadmap. Continue with the course to unlock more practical strategies for building and shipping your next set of features.