What You Will Learn

• Prototype a working SPA using Lovable from a structured prompt
• Iterate, refactor, and add features using Cursor
• Craft prompts and apply service-based and hexagonal architecture patterns
• Version projects in GitHub and deploy live with Vercel
• Test with mock data, run a local dev server, and generate OpenAPI schemas

Study Guide

Introduction: The New Era of Rapid Prototyping with AI Agents

Welcome to "Vibe Coding Session with Lovable and Cursor AI Agents." This course is your hands-on manual for transforming how you build applications,from napkin sketches to working MVPs,using cutting-edge AI agents. If you’re looking to accelerate your prototyping, minimize costs, and unlock a more fluid, experimental approach to software development, you’re exactly where you need to be.

You’ll learn how Lovable and Cursor AI agents, paired with essential tools like GitHub and Vercel, can compress the traditional, costly stages of app development into a streamlined, creative, and iterative workflow. By the end, you’ll know how to leverage AI for everything from generating structured code to deploying full-fledged applications, all while avoiding anti-patterns and setting the stage for maintainable, scalable products.

The world doesn’t wait for perfect. It rewards those who can test, iterate, and launch quickly. This course gives you the playbook to do just that,with clarity, depth, and a dose of creative freedom.

The Problem with Traditional Application Development

Before AI agents, building software followed a rigid, expensive path: mockup, proof of concept, and MVP, each requiring escalating investments of time and money. Let's break down these stages:

1. Mockup: A visual sketch or static prototype. Cheap to create. Think Figma designs or paper wireframes that show what the app might look like but don’t actually work.
2. Proof of Concept (POC): A barebones, working version built to test feasibility. It demonstrates whether the idea can be built, but it’s not ready for real users. More complex than a mockup, and therefore, more expensive.
3. Minimum Viable Product (MVP): A functional app with core features, good enough for early users. This is the most expensive and time-consuming stage, as it requires actual development, testing, and deployment.

Example 1: Imagine a founder wants to build a marketplace app. Traditionally, she’d pay for Figma mockups, then hire a dev to build a basic listing page as a POC, and finally invest in a team to make the full MVP. Each step means waiting and spending more.
Example 2: A corporation needs an internal dashboard. They start with a design sprint (mockup), then a dev creates a clickable prototype (POC), and after sign-off, the MVP gets built. The process is linear, slow, and expensive.

How AI Agents Collapse the Old Process

AI agents like Lovable and Cursor change the game by bundling these stages into a single, fluid process. Instead of mockup → POC → MVP, you can now prototype, iterate, version, update, and deploy,often in days, not months. You pay for an AI subscription or a fixed fee, and the agents handle what used to require whole teams.

Let’s visualize the AI-driven workflow:

1. Prototype: Instantly generate a working front end from a detailed prompt.
2. Iterate: Make rapid improvements with AI guidance,change layouts, add features, fix bugs.
3. Version: Move your code to GitHub for tracking and collaboration.
4. Update: Continue refining using AI agents, adding new functions or refactoring code.
5. Deploy: Launch to a live environment with a button click.

Example 1: You describe a SaaS dashboard in a prompt to Lovable. Within minutes, you get a working UI,no manual coding, just a smart translation from requirements to product.
Example 2: Need to theme your app or add a login form? Cursor AI can make targeted changes in seconds, iterating as you refine your vision.

Meet Your AI Agents: Lovable and Cursor

Lovable and Cursor are specialized AI agents built for different parts of the workflow. Understanding their roles is key to maximizing your results.

• Lovable: Best for initial prototyping and front-end generation. You feed it a detailed prompt, and it produces structured React code, often as a single-page application (SPA). Lovable can connect with mock databases (e.g., Superbase) and is driven by clear technical specs.
• Cursor: Ideal for code iteration, updates, and targeted edits. Cursor operates like a developer’s assistant who can refactor code, implement new features, and generate documentation,especially useful for nuanced or advanced changes.

Example 1: Use Lovable to create the skeleton of a job board app, specifying you want service-based code structure.
Example 2: After the prototype, use Cursor to implement a dark mode toggle or to generate an OpenAPI schema for your backend.

Prompt Engineering: The Secret Sauce

AI agents are only as good as the prompts you give them. Clear, structured prompts with architectural direction produce better, more maintainable code. Here’s how to harness this power:

• Be Specific: Define what you want in terms of features, pages, and user flows.
• Use Architectural Keywords: Mention SPA, service-based patterns, hexagonal units, DTOs (Data Transfer Objects), repositories, and adapters. This nudges the AI toward scalable design.
• Negative Constraints: Clearly state what you DON'T want (e.g., “Do not attempt to implement the back end. Treat the front end as a SPA. Do not have the client directly access the database.”)

Example 1: “Create a three-page finance tracker SPA. Use DTOs, repositories, adapters for data access. Only generate the front end. Use mock data for transactions.”
Example 2: “Build a platform for group event planning with a calendar and RSVP feature. Do not implement backend logic. Use a services-based architecture and separate UI from data models.”

Tips:
- Use prompt templates for common patterns.
- Iterate on your prompts as you learn what the AI responds to best.
- Include user stories and technical specs for clarity.

Structured Code Generation: Building for the Long Run

AI code is fast, but structure is what makes it usable and maintainable. The course emphasizes service-based patterns, hexagonal architecture, DTOs, repositories, and adapters. Here’s what that means:

• Service-Based Patterns: Organize your code into independent services for different features (e.g., user management, payments).
• Hexagonal Architecture (Ports & Adapters): Separate your core app logic from external dependencies. This means your business logic doesn’t depend directly on the database or UI.
• DTOs, Repositories, Adapters: Use DTOs to transfer data between layers, repositories to abstract database access, and adapters to connect services.

Example 1: In a task manager app, you have a TaskService for business logic, a TaskRepository for storing tasks, and TaskDTOs for passing data between the UI and service. Adapters connect the TaskService to an external calendar API.
Example 2: For an e-commerce front end, you use ProductDTOs for product info, ProductRepository for data fetching, and services for cart management. The front end talks only to the API, never to the database directly.

Best Practices:
- Always keep your UI logic separate from data fetching and business logic.
- Avoid “spaghetti code” by sticking to clear separation of concerns.
- Review the AI’s output for anti-patterns,especially database access from the client.

Avoiding Anti-Patterns in AI-Generated Code

AI agents can move fast, but they can also create security or maintenance headaches if unchecked. One major anti-pattern: letting the front end access the database directly. Always use an API service pattern.

Example 1: If the generated React code fetches data directly from a SQL endpoint, stop and refactor to use a REST API.
Example 2: Suppose Lovable tries to connect the UI to a Supabase table directly. Instead, generate mock data or an API service layer for the front end.

Why This Matters: Direct database access exposes sensitive data, breaks separation of concerns, and makes your app harder to scale or secure.

From Prototype to Version Control: Why GitHub Matters

Once you have a working prototype, get it into GitHub immediately. Version control isn’t just for big teams,it’s your safety net, collaboration platform, and project archive.

• Track Changes: See who changed what, when, and why. Roll back if something breaks.
• Collaborate: Multiple developers (or AI agents) can contribute, review, and merge code.
• Integrate with Tools: Easily connect with deployment platforms like Vercel, automate builds, and add CI/CD.

Example 1: After generating a job board prototype with Lovable, push the code to a new GitHub repo. Set up branches for feature development.
Example 2: Use GitHub’s pull request system to merge changes made by Cursor AI or other team members. Track issues and feedback.

Tips:
- Organize code in a dedicated “projects” directory,not on your desktop.
- Write clear commit messages.
- Use .gitignore to avoid uploading sensitive files (e.g., environment variables).

Iterating and Updating with Cursor AI

Cursor AI shines in the code iteration phase. Whether it’s changing themes, adding authentication, or fixing bugs, Cursor acts as a developer who never sleeps.

• Targeted Edits: Ask Cursor to switch your app to dark mode, add a new page, or refactor components.
• Advanced Features: Generate OpenAPI schemas, document code, or add test coverage.

Example 1: “Switch the main navigation and UI to dark mode.” Cursor finds the relevant CSS or theme files and implements the change.
Example 2: “Generate an OpenAPI schema for the endpoints needed in this app.” Cursor creates the OpenAPI YAML or JSON, ready for backend development.

Tips:
- Use Cursor for fine-tuning after the initial prototype.
- Test changes locally before pushing to GitHub.
- Review generated code for structure and clarity.

Mock Data: Testing Without a Backend

During early prototyping, you rarely have a real backend. Mock data solves this, letting you simulate user flows and UI states long before the database is live.

• Faster Feedback: See how the app behaves with realistic data sets.
• UI Testing: Validate layouts and user interactions without waiting for backend APIs.

Example 1: Use an array of fake transactions to test your finance app’s dashboard.
Example 2: Populate a calendar with mock events to preview user experience in your event planner.

Best Practices:
- Clearly mark mock data in your codebase.
- Replace with real APIs once backend is ready.
- Use tools like JSON Server or MirageJS if you need more advanced mocking.

Running a Local Dev Server: Your Testing Ground

After AI agents generate your code, run it locally using a dev server. This is where you catch bugs, test features, and see your app in action.

• Immediate Feedback: See real-time changes as you edit code or prompts.
• Debugging: Use browser dev tools to inspect elements, console logs, and network requests.

Example 1: Use npm start or yarn dev to launch your React app locally and test login flows.
Example 2: Preview theme changes (like dark mode) in your browser before deploying.

Tips:
- Always check the local version before pushing changes to GitHub.
- Use tools like hot reloading for faster iteration.
- Test on multiple browsers/devices if possible.

Deployment: From Localhost to Live in Minutes with Vercel

When your app is ready to meet the world, deployment should be seamless. Vercel integrates tightly with GitHub, letting you deploy updated versions with a click or even automatically on new commits.

• Continuous Deployment: Every push to main/master can trigger a new deployment.
• Preview URLs: Share live previews for feedback before going public.
• Easy Rollbacks: Instantly revert to previous versions if needed.

Example 1: Connect your GitHub repo to Vercel. On push, Vercel builds and deploys your app to a live URL.
Example 2: Use vercel.json to configure custom routes or environment variables.

Tips:
- Test your app in the Vercel preview environment before going live.
- Use custom domains for production versions.
- Automate deployments for every main branch push.

Generating OpenAPI Schemas: Setting Up the Backend

Even if you start with front-end prototyping, you’ll eventually need backend APIs. Cursor can generate OpenAPI (Swagger) schemas, providing a contract for backend developers and clear documentation.

• API Blueprint: Define endpoints, request/response types, and authentication in a standard format.
• Documentation: Use the schema to auto-generate API docs for your team or users.

Example 1: “Cursor, generate an OpenAPI schema for a task manager with endpoints for createTask, listTasks, and deleteTask.”
Example 2: “Create a schema for a booking system with endpoints for listRooms, bookRoom, and cancelBooking.”

Best Practices:
- Keep your schema up to date with new features.
- Share schemas with backend teams early.
- Use tools like Swagger UI to visualize and test endpoints.

Vibe Coding: The Art of Experimental, Fast-Paced Development

“Vibe coding” is about jumping in, experimenting, and iterating quickly,getting a feel for the app, not just following a checklist. AI agents amplify this approach, letting you try ideas, test flows, and pivot as needed.

• Rapid Prototyping: Don’t overthink the first draft. Generate, test, and refine.
• Feedback Loops: Show prototypes to users or stakeholders early and often. Update based on real reactions.
• Creative Freedom: Use the AI to explore multiple UI layouts, feature sets, or workflows without the sunk cost of manual coding.

Example 1: Generate three different landing pages with Lovable and pick the one that resonates most.
Example 2: Use Cursor to quickly prototype a chat feature, test it with users, and iterate based on feedback.

Tips:
- Don’t aim for perfection on the first try.
- Use AI agents as creative collaborators, not just code generators.
- Document what works and what doesn’t for future reference.

Cost and Time Savings: The AI Advantage

Traditional app development is expensive,often six figures for a robust prototype. AI-driven workflows can deliver working prototypes for a fraction of the cost, sometimes as little as a few hundred dollars if you’re efficient with prompts and tools.

• Fixed Subscription Costs: Pay monthly for AI tools instead of per-hour developer rates.
• Rapid Turnaround: Move from idea to live app in days rather than months.
• Lower Risk: Test more ideas with less investment, discarding what doesn’t work.

Example 1: A founder builds and launches a prototype SaaS for $100 using AI agents, validating the idea before investing more.
Example 2: A corporate team creates internal tools for multiple departments using a single AI subscription, eliminating the need for external contractors.

Hidden Costs to Watch For:
- Advanced features like authentication or payments may still require manual coding and collaboration.
- Over-reliance on AI can lead to technical debt if code isn’t reviewed for structure.
- Some integrations (e.g., AWS, advanced databases) may require additional setup or costs.

Complexity and Collaboration: What Still Needs Human Touch

AI agents are powerful, but not magic. Complex features,like authentication, payment processing, and business-specific workflows,still require careful human input and iteration.

• Authentication: Implementing secure login, password recovery, and user management can be tricky. Expect to work closely with your team or AI agent to get it right.
• Payments: Integrating Stripe, PayPal, or other processors involves handling sensitive data, compliance, and edge cases.
• Custom Logic: Business rules, integrations with legacy systems, or unique workflows often need detailed human oversight.

Example 1: Use Cursor to scaffold an authentication flow, then review and refine with a security expert.
Example 2: Prototype a payment form with Lovable, then work with a backend dev to implement PCI-compliant processing.

Tips:
- Use AI for the first pass, then iterate manually for complex features.
- Collaborate with stakeholders to clarify edge cases and requirements.
- Treat AI-generated code as a starting point, not a finished product for critical features.

Expanding Your Toolset: Other AI and Dev Tools

While Lovable and Cursor are front-and-center, the workflow can involve other tools for specialized needs:

• Devon: AI for handling background tasks, pull requests, and automation.
• Superbase: Backend-as-a-service for quick database setup, often paired with Lovable.
• Replit, v0: Alternative platforms for prototyping and code sharing.
• AWS and SST: For advanced deployment, especially at scale.
• Klein, Rode: Mentioned as part of the evolving AI tool ecosystem.

Example 1: Use Superbase to quickly add real-time data to your app once the front end is stable.
Example 2: Automate pull request checks with Devon, freeing you to focus on features.

Tips:
- Choose the right tool for the job; don’t get locked into a single platform.
- Stay updated on new AI tools that can accelerate your workflow.
- Integrate with cloud providers (AWS, Vercel) for smoother production deployments.

Glossary: Key Terms You Need to Know

To make sure we’re speaking the same language, here’s a quick rundown of essential concepts:

• AI Agent: Software that performs tasks autonomously, such as generating or editing code based on prompts.
• Mockup: Static visual of a UI or product.
• POC (Proof of Concept): Limited project to test feasibility.
• MVP (Minimum Viable Product): Core product with basic features for early users.
• Prototype: Initial version to demo core functionality.
• Iterate: Refining and improving through repetition.
• Version Control: Tracking changes, collaboration, and rollback using tools like GitHub.
• Deploy: Making the app accessible online.
• SPA (Single Page Application): Web app that updates dynamically from a single HTML page.
• Service-Based Pattern: App organized as independent services.
• Hexagonal Unit: Architecture separating core logic from external dependencies.
• DTO: Object for transferring data between layers.
• Repository: Encapsulates logic for data access.
• Adapter: Connects core logic to external services/interfaces.
• Mock Data: Simulated data for testing.
• Dev Server: Local server for development/testing.
• OpenAPI Schema: Standard for describing RESTful APIs.
• Vibe Coding: Experimental, rapid approach to development.
• Anti-pattern: Ineffective/counterproductive coding practice (e.g., direct DB access from the front end).

Applying the Agile AI Agent Workflow: Step-by-Step

Let’s walk through the full workflow, from idea to deployment, using everything you’ve learned:

1. Prototype with Lovable: Write a detailed, structured prompt. Specify features, pages, and architecture. Generate the initial SPA front end with mock data and proper service patterns.
2. Iterate Rapidly: Use Lovable or Cursor to make changes,add UI elements, tweak layouts, or fix bugs. Run the local dev server to test updates in real time.
3. Version in GitHub: Move your code to a clean projects directory. Push to GitHub, create branches for features, and use pull requests to manage changes.
4. Update with Cursor: Refine code, implement advanced features (like dark mode or new endpoints), and generate API schemas for backend planning.
5. Deploy with Vercel: Connect your repo, configure deployment, and launch. Use preview URLs for feedback, and iterate as needed.

Example 1: Building a fitness tracker: Generate the main dashboard with Lovable, iterate on workout logging with Cursor, move to GitHub, and deploy to Vercel.
Example 2: Creating a booking app: Prototype the booking UI, refine with user feedback, version in GitHub, have Cursor generate OpenAPI schemas, and deploy to a live domain.

Best Practices for Sustainable AI-Driven Development

To ensure your rapid progress doesn’t create future headaches, follow these best practices:

• Review AI Output Carefully: Scan for anti-patterns, security issues, or code smells.
• Separate Concerns: Keep UI, business logic, and data access clearly separated.
• Document as You Go: Use AI to help generate docs and comments, but always review for accuracy.
• Test Early and Often: Use mock data and local dev servers to validate features before user testing.
• Iterate Relentlessly: Don’t wait for perfection,launch, learn, and improve.

Example 1: Add automated tests as soon as your app stabilizes, using Cursor to scaffold them.
Example 2: Regularly refactor AI-generated code to maintain structure and readability.

Key Takeaways: The Future is Fluid, Fast, and Experiment-Driven

You now hold the keys to a new style of software development,one that’s collaborative, creative, and radically accelerated by AI agents.

• The old, costly stages of development are being replaced by a single, agile workflow.
• AI agents like Lovable and Cursor can generate, iterate, and refine code faster than ever,if you know how to guide them.
• Structured, well-prompted code is essential for long-term maintainability and easier backend integration.
• Version control and deployment platforms tie the workflow together, providing safety, collaboration, and speed.
• While AI accelerates the easy parts, complex features still need thoughtful human input and collaboration.

The most important thing you can do now is to apply these skills: experiment, build, iterate, and launch. Don’t wait. Use AI as your creative partner, and let your ideas become working products in record time.

Every hour you save on manual coding is an hour you can spend refining your vision, talking to users, and building something that matters. The future belongs to those who ship,fast, often, and with style.

Frequently Asked Questions

This FAQ section is designed to clarify concepts, address practical workflow steps, and provide guidance for anyone interested in the "Vibe Coding Session with Lovable and Cursor AI Agents." If you’re looking for honest answers, actionable tips, and real-world context for using AI agents in application development, you’re in the right place. Whether you’re new to AI-powered coding or want to refine your workflow, these FAQs are structured to move from the basics to more advanced topics, including practical advice and common pitfalls.

How can AI agents streamline the application development process?

AI agents can significantly condense the traditional staged approach to application development (mock-up, proof of concept, MVP) into a more unified process.
Historically, these stages were separate due to cost considerations, with mock-ups being the cheapest and MVPs the most expensive. AI agent platforms, often with a fixed cost or subscription, allow developers to combine these steps, accelerating the initial prototyping and iteration phases. This means you can go from idea to functional prototype much faster, enabling rapid experimentation and feedback.

What is the proposed workflow for developing an application using AI agents?

The core workflow involves several key steps: prototyping, iterating, versioning, updating, and deploying.
You typically start by generating a prototype using an AI agent like Lovable, iterate on it with tools like Replit or v0, manage versions using GitHub, make subsequent updates with AI agents such as Cursor AI agents, and finally deploy to platforms like Vercel or AWS. Each phase leverages the strengths of different tools, creating a streamlined and effective development cycle.

How important is good code structure when using AI agents for code generation?

Generating structured code is crucial when using AI agents.
A well-organised codebase, following patterns like the hexagonal architecture (using DTOs, repositories, and adapters) and a services-based pattern, makes it easier for developers to understand, modify, and build upon the AI-generated code later. This is especially important when adding backend functionalities, integrating APIs, or scaling the project. A solid structure reduces technical debt and simplifies future changes.

What are the advantages of transferring an AI-generated prototype to a version control system like GitHub?

Transferring an AI-generated project to GitHub is a crucial step for effective version control.
This enables you to track changes, revert to previous states, and collaborate with others. While AI agent platforms are excellent for quick iterations, moving to GitHub provides a more reliable environment for managing the project’s evolution and supports further development and updates using other tools or AI agents like Cursor. It also encourages best practices and team collaboration.

How can Cursor AI agents be used to make updates to an application after the initial prototype is generated?

Once a project is in a GitHub repository, Cursor AI agents can be used to make specific updates easily.
For example, you can instruct a Cursor agent to switch the application's theme to dark mode. Cursor agents are effective for detailed code-level work, allowing you to implement changes, fix errors, and refine the codebase after the initial generation phase. This keeps development agile and responsive to feedback, even after going live.

What are some key architectural principles highlighted for building applications with AI agents?

Several architectural principles are important, even with AI-generated code.
These include treating the frontend as a Single Page Application (SPA), using a services-based pattern for data fetching, employing hexagonal architecture (using DTOs, repositories, and adapters), and avoiding direct coupling between the UI and the data model. These principles make applications more maintainable and scalable, allowing easier changes and integrations down the line.

How can an AI agent assist in the deployment process?

AI agents can automate and simplify deployment.
For instance, a Cursor AI agent can handle tasks like creating configuration files (such as vercel.json) and walk you through deploying to platforms like Vercel. While some CLI tools may need to be installed, the AI agent can guide you step by step, making deployment smoother for both beginners and experienced developers.

What is "vibe coding" and how does it relate to using AI agents?

"Vibe coding" refers to a more exploratory, fluid approach to development, often facilitated by AI agents.
It’s about generating ideas quickly, experimenting with solutions, and getting a feel for the problem before focusing on details. By using AI agents for rapid prototyping and iteration, you can "vibe code" to explore possibilities, then shift to more focused development when the direction becomes clearer. This approach accelerates creativity and learning.

What are the traditional stages of application development, and how has AI changed them?

The traditional stages are Mockup, Proof of Concept (POC), and Minimum Viable Product (MVP).
AI agents can bundle these stages into a more unified, cost-effective process. Instead of spending time and budget on each stage separately, AI platforms can generate prototypes that closely resemble MVPs, reducing overhead and time to market.

What are the five steps in the agile AI agent workflow?

The five steps are: Prototype, Iterate, Version, Update, and Deploy.
You start by prototyping the application, refine it through iteration, transfer the project to version control (like GitHub), continue updating and enhancing, and finally deploy it to a hosting platform (such as Vercel or AWS). This creates an efficient, feedback-driven loop for continuous improvement.

Which AI agent platforms are featured in the workflow demonstration?

Lovable and Cursor AI are specifically highlighted for their capabilities in prototyping and code updates.
Lovable is used for generating the initial front-end prototype, while Cursor AI excels at detailed code changes and ongoing development. Other tools like Replit, v0, Devon, Klein, and Rode are mentioned as alternatives for various workflow stages.

Why is GitHub an important part of the AI agent coding workflow?

GitHub enables version control, collaboration, and history tracking of your project.
After initial prototyping, moving your code to GitHub allows you to safely manage changes, roll back to previous versions if needed, and work with others seamlessly. This is especially important for teams and for maintaining high-quality codebases as projects evolve.

What is a Single Page Application (SPA), and why treat the front end as one?

An SPA is a web application that loads a single HTML page and updates content dynamically without full page reloads.
Treating your front end as an SPA keeps the experience fast and seamless for users. It also allows the front end to interact with the back-end API for data, making the architecture more flexible and scalable.

What are DTOs, repositories, and adapters, and why are they important?

DTOs (Data Transfer Objects), repositories, and adapters are architectural patterns that help structure your codebase.
DTOs move data between layers without exposing internal models. Repositories handle data access, and adapters connect your core logic to external services. Using these patterns helps avoid tight coupling, making your codebase easier to test, maintain, and extend.

Why should the back end be implemented separately via an API instead of direct database access?

Having the front end directly access the database is considered a major anti-pattern.
This setup can cause security, maintainability, and scalability problems. By building the back end as an API, you establish a clear separation of concerns, safeguard data, and make future integrations or changes less risky.

What is the purpose of running a local development server after transferring code from Lovable?

Running a local development server allows you to test and view your application locally in a browser.
This step helps you catch bugs, experiment with design changes, and validate user flows before deploying. It’s a critical part of the iteration phase, ensuring your app works as intended in a real environment.

How was dark mode implemented in the application demonstration?

Dark mode was enabled by instructing the Cursor AI agent to switch the application's theme.
This shows how AI agents can handle specific, targeted updates to the codebase, streamlining feature implementation without manual code rewriting.

What is the benefit of generating an OpenAPI schema for the API service?

An OpenAPI schema provides a structured definition of your API endpoints.
This serves as both documentation and a starting point for back-end development, helping teams understand how to interact with the API and generate client code or documentation automatically.

What is prompt engineering, and why does it matter in AI agent coding?

Prompt engineering is the process of crafting clear, specific instructions for AI agents to follow.
A well-designed prompt guides the AI to generate the desired output,whether that’s a front-end prototype, an architectural pattern, or a code update. Including negative constraints (like “do not implement the back end”) and specifying frameworks or design patterns can lead to higher quality, more relevant code.

How can you use mock data in AI agent-generated applications?

Mock data provides simulated responses for testing your UI and user flows before real back-end integration.
Using mock data lets you experiment with front-end components, refine the user experience, and validate app logic without relying on a finished back end. This is especially useful during rapid prototyping.

Why is it recommended to use a projects directory instead of the desktop for code storage?

Organising your projects in a dedicated directory keeps your workspace clean and your files easier to manage.
This makes it simpler to find, back up, and version control your work, reducing the risk of accidental deletions or confusion. It’s a small habit that pays off as your codebase grows.

How do AI agents help save costs in application development?

AI agents reduce the time and resources needed for prototyping, iteration, and MVP development.
With a fixed cost or subscription, you can quickly generate and refine working prototypes, cutting down on manual coding hours and expensive design cycles. However, watch for potential hidden costs like subscription fees, integration challenges, or the need for human review.

What are some common challenges when using AI agents for development?

Some frequent challenges include code quality, unexpected outputs, and integration issues.
AI-generated code may need to be refactored for maintainability or compliance with your team’s standards. Integrating new features or third-party services can require additional prompt engineering or manual intervention. Testing and code review remain crucial for catching subtle bugs or architectural mistakes.

How do you handle complex features like authentication or payments when using AI agents?

AI agents can provide a starting point for complex features but may require additional clarification or collaboration.
Features like authentication, payments, or complex business logic often involve multiple steps, security considerations, and external integrations. Working closely with your team and using clear prompts helps ensure these features are implemented correctly. Manual review and testing are essential here.

Can AI agents integrate with databases like Superbase?

Some AI agents, such as Lovable, are known for potentially integrating with databases like Superbase.
While direct integration may not always be fully automated, you can use AI-generated code as a foundation and then connect it to Superbase or other databases by specifying clear prompts and following up with manual configuration where necessary.

What are some best practices for collaborating on AI agent-generated projects?

Use version control (like GitHub), communicate clearly, and review code regularly.
Establish conventions for code structure and documentation. When working with AI agents, ensure team members understand the workflow and can contribute to prompts or code reviews. Collaboration tools like pull requests and issue tracking help maintain quality and transparency.

How do you ensure AI-generated code meets your organisation’s standards?

Combine AI agent outputs with thorough code reviews and adherence to internal guidelines.
Set clear expectations in your prompts regarding formatting, architectural patterns, and documentation. Regularly review and refactor AI-generated code to align with your team’s best practices and compliance requirements.

What role does prompt refinement play in AI agent productivity?

Prompt refinement is key to getting better, more relevant outputs from AI agents.
If the initial result doesn’t match your expectations, adjust your prompt with more details, constraints, or examples. Small changes in wording can lead to significant improvements in code quality or feature completeness.

Can AI agents handle backend implementation, or should this be done manually?

AI agents can assist with backend scaffolding but may require manual intervention for complex logic or integrations.
It’s often best to let AI agents generate the initial API structure or models, then refine the backend manually to address business rules, security, or integration points. A hybrid approach leverages the speed of AI with the expertise of human developers.

How do you version control AI agent-generated code effectively?

Adopt the same version control practices you would for any project: use GitHub, commit early and often, and write meaningful commit messages.
Create branches for new features or experiments, and use pull requests for code reviews. Tag releases and document major changes to keep your project organised and traceable.

What is the role of deployment platforms like Vercel or AWS in this workflow?

Deployment platforms like Vercel or AWS make your application accessible to users with minimal setup.
AI agents can automate deployment scripts and configuration files, allowing you to push your code live quickly. These platforms also handle scaling, SSL, and other infrastructure concerns, so you can focus on development instead of server management.

Are there any pitfalls to avoid when using AI agents for code generation?

Common pitfalls include over-reliance on AI, neglecting code reviews, and skipping documentation.
Always review generated code for security, maintainability, and compliance issues. Don’t assume the AI’s output is bug-free. Make sure to document both the workflow and the codebase for future developers.

How does iteration fit into the AI agent development workflow?

Iteration is central,AI agents make it easy to rapidly refine your application through repeated cycles of testing and feedback.
By making small, incremental changes and reviewing the results, you can polish features, fix bugs, and adapt to user needs far more efficiently than with traditional processes.

How can business professionals who aren’t developers benefit from this workflow?

AI-powered workflows let business professionals prototype ideas and experiment with features without deep coding knowledge.
By clearly describing requirements and desired outcomes, non-developers can leverage AI agents to create proofs of concept, validate assumptions, and communicate more effectively with technical teams. This reduces the barrier between idea and implementation.

What should you do if an AI agent generates incomplete or incorrect code?

If the output isn’t what you expected, refine your prompt, provide more context, or specify constraints.
Don’t hesitate to make manual corrections or leverage code reviews within your team. Iteration is part of the process,AI agents improve with clearer instructions and feedback.

How do you test an AI agent-generated application before deployment?

Run your application locally using a development server, perform user flow testing, and write automated tests if possible.
Check for bugs, broken links, and UI inconsistencies. Encourage team members to review and provide feedback. Testing ensures your application works as intended before going live.

Can AI agents generate documentation, or does this require manual effort?

AI agents can generate basic documentation, such as README files or API references, if prompted.
You may still need to refine and expand this documentation for clarity and completeness. Combining AI-generated drafts with manual edits leads to better resources for your team and users.

How do you handle bug fixes and feature requests in an AI agent-centric workflow?

Open issues or tasks in your version control platform, then use AI agents to implement or suggest code changes.
Prompt the AI with specific bug descriptions or feature requirements. Review and test the output before merging. This keeps your workflow responsive and organised.

What types of projects are best suited for development with AI agents?

AI agents excel at rapid prototyping, MVPs, internal tools, and standard web applications.
Projects with clear requirements and well-established patterns see the most benefit. Highly custom or complex systems may require more manual oversight, but AI agents can still speed up initial scaffolding and basic features.