Full-Stack RAG with Next.js, Supabase & Gemini

Retrieval-Augmented Generation (RAG) is one of the most practical ways to build AI applications that answer questions from your own data rather than relying on a model's training knowledge alone. This free course walks you through every layer of a production-ready RAG system: the foundational theory of how large language models work with vector embeddings, the full indexing pipeline that turns documents into searchable knowledge, and the retrieval and generation loop that powers a grounded, citation-aware chatbot. You will work with Next.js for the frontend and API layer, Supabase with pgvector for vector storage and Postgres-native semantic search, and Google's Gemini API for text generation.

The course is structured around two complementary phases. First, you learn to build the knowledge base: chunking documents effectively, vectorizing and storing them in Supabase, and writing Postgres RPC functions for similarity search. Then you move into the RAG core itself, covering retrieval science, prompt engineering for grounding, streaming responses from Gemini, and wiring everything together through a secure Next.js backend with row-level access controls. Later modules address the realities of shipping software: conversational RAG with memory, attribution and citations in the UI, and techniques for improving retrieval quality while managing cost and latency.

This is an advanced course suited to JavaScript or Next.js developers who already have a working grasp of web development and want to add a sophisticated AI feature to a real product. No prior experience with RAG or vector databases is assumed, but comfort with TypeScript and API routes will help you move quickly. Finishing the course and passing the final exam earns a certificate of completion you can share on LinkedIn or add to a resume.