Build an AI Chatbot
Chatbots are the most-requested AI feature — and the most often done badly. This course teaches you to build one that is actually useful: a chatbot with a knowledge base, streaming responses, conversation memory, and tool use. You will build a working chatbot by lesson 3 and progressively harden it into something production-ready by lesson 8.
What you'll learn
Course outline
Free — no account needed
What Makes a Chatbot Actually Useful
The anatomy of a good chatbot — and the design decisions that separate useful from annoying
Chatbot Architecture — Retrieval, Context, and Memory
How a production chatbot actually works under the hood — before you write a single line
Your First Chatbot — Streaming Responses in Next.js
Build a working Claude-powered chatbot with streaming in under an hour
Full course — $59 one-time
Giving Your Chatbot a Knowledge Base
Add RAG so your chatbot answers questions from your own docs, products, or FAQs
Conversation Memory — Short-Term and Persistent
Build a chatbot that remembers context across turns and across sessions
Tool Use — Chatbots That Take Actions
Give your chatbot the ability to search, look up data, and trigger real-world actions
Streaming, Loading States, and a Great UX
Make your chatbot feel fast and polished — typing indicators, error states, and smooth streaming
Deploying and Monitoring Your Chatbot in Production
Rate limiting, cost control, error monitoring, and going live on Vercel
Get the full course
8 lessons — from first chatbot to RAG, tool use, and production deployment.
Written by the RadarTrek editorial team · Reviewed June 2026
About this course
Building an AI chatbot means more than wiring up an LLM API and displaying responses — it means handling conversation history, streaming, authentication, rate limiting, and designing a UX that feels trustworthy. This AI chatbot tutorial walks through building a production-ready chatbot from scratch: a streaming chat UI, persistent conversation history in a database, user authentication, and deployment. By the end you will have built the same core architecture powering most commercial AI chat products.
AI chatbots are now standard features in SaaS products, customer support tools, internal knowledge bases, and developer tools. Knowing how to build one end-to-end is a differentiating skill. This course is for developers who want to ship AI chat features and founders who want to understand what building a chatbot actually involves.
Frequently asked questions
What technology stack does this course use?
This course uses Next.js for the full-stack application, the Vercel AI SDK for streaming and AI integration, Supabase for conversation history storage and authentication, and Claude as the AI model. These represent the most common production stack for AI web applications in 2026. The patterns you learn transfer to other stacks — streaming, history management, and auth are universal concepts.
What is streaming and why does it matter for chatbots?
Streaming means sending the AI response word by word as it is generated, rather than waiting for the complete response. Without streaming, users stare at a blank screen for several seconds. With streaming, text appears immediately and the interaction feels much more responsive. Implementing streaming correctly is one of the first things that makes an AI chatbot feel polished rather than prototype-grade.
How do I store conversation history?
Conversation history needs to be stored in a database so users can return to previous conversations and so the AI has context from earlier in the chat. This course uses Supabase (PostgreSQL) with a messages table linked to conversations and users. Each message stores the role (user or assistant), content, and timestamp. The chat API retrieves recent messages and includes them in the context sent to the AI model.
How do I prevent abuse and manage costs on a public chatbot?
Rate limiting (limiting requests per minute per user), authentication (only logged-in users can access the API), and token budgeting (capping monthly token consumption per user) are the three main levers. This course covers implementing rate limiting at the API route level and tracking token usage per user in the database so you can apply soft and hard limits.
Can I customise the chatbot to answer questions about my specific domain?
Yes — customising the chatbot behaviour is done primarily through the system prompt. You can define the persona, restrict it to specific topics, provide background knowledge about your product, set the tone, and specify what to do when asked out-of-scope questions. For grounding the chatbot in specific documents, the RAG and Vector Search course covers retrieval-augmented generation.