Chatbot Website: AI Chat Platform

Exploring and deploying Vercel's AI Chatbot template as a first step into AI development.

DEMO LINK

Overview

• Role: Developer / Integrator
• Stack: Next.js 14, TypeScript, Tailwind CSS, Vercel AI SDK, NextAuth.js
• Key Result: Successfully configured and deployed a complex, multi-provider AI template.
• Summary: This was my first hands-on AI project. Instead of reinventing the wheel, I utilized Vercel’s open-source AI Chatbot template. I explored its architecture, made a few personal tweaks, and focused on successfully configuring and deploying it to learn how modern AI applications operate.

The Problem

• Challenge: Understanding a large, pre-existing codebase (Vercel's template) and figuring out how to configure its environment variables, database connections, and AI providers for a live deployment.
• Audience: Myself (for learning) and portfolio visitors wanting to test a live AI chat.
• Constraints: Navigating the learning curve of the Vercel AI SDK and setting up serverless persistence for the first time.

Approach & Decisions

• Architecture: Leveraged the template's out-of-the-box Modular Next.js 14 App Router setup and React Server Components.
• Key Decisions: Decided to use a robust, MIT-licensed template as a learning sandbox. This allowed me to study production-ready code rather than struggling to build basic streaming mechanics from scratch.
• Trade-offs: Sacrificed complete customizability in favor of having a reliable, fully-featured starting point to study and deploy.
• Data Flow: Studied how the template routes user input through the Vercel AI SDK to different LLM Providers and streams the response back to the UI.

Key Results & Impact

• Outcome: Gained practical experience in configuring, tweaking, and deploying a modern Next.js 14 application with AI capabilities.
• Impact: Demystified the process of integrating LLMs into web apps, serving as a solid foundation for my future, more customized AI projects.

Technical Deep Dive

(Note: These are features native to the Vercel template that I explored and learned from during this project.)

• Notable Details: Explored the template's built-in artifacts system which handles code, images, and notes.
• Verification: Learned how the project utilizes Playwright for End-to-End (E2E) testing out of the box.
• Optimizations: Studied how lazy loading and React Server Components keep the chat interface performant during active streaming.
• Code Quality: Reviewed and learned from the project's strongly typed, component-driven structure.

Reflections

• Differently: In future projects, I plan to build my own custom UI on top of the AI SDK from scratch now that I understand the core concepts.
• Successes: Successfully managed the environment configurations, connected the necessary databases, and got the live demo running smoothly.
• Lessons: Reading and understanding other developers' production-ready code is one of the fastest ways to learn new technologies like the Vercel AI SDK.

My Contributions

• Configuration & Deployment: Set up environment variables, connected the necessary databases (Neon Serverless Postgres / Vercel Blob), and deployed the template to a live environment.
• Code Exploration: Deeply explored the AI SDK architecture and LLM extensibility patterns provided by the template.
• Tweaks: Made minor personal adjustments to the UI/UX and provider connections to suit my specific demo needs.

Project Architecture

This project utilizes the architecture provided by the Vercel template:

• Component Organization:
  • app/: Next.js routes handling the core chat and auth flows.
  • components/: Pre-built, reusable UI components.
  • lib/ai/: Configuration files for LLMs and prompt logic.
• State Management: Uses React Context and Server Actions for state transitions.
• Integrations: Powered by the Vercel AI SDK, NextAuth for authentication, and serverless databases for chat history.

Demo Preview

You can view the live, tweaked deployment at chatbot.franzdomingo.dev. All features and UI are available to explore directly.

• Web View: Responsive, mobile-first design.
• Real-time Chat: Test the streaming capabilities of the integrated LLMs.
• Artifacts: Experience how the chatbot generates code and notes on the fly.

Hosting Note:
Demo is hosted at chatbot.franzdomingo.dev using a proprietary API setup. Source code is based on Vercel's template; contact the author via franzdomingo.dev for details.