Building a RAG application in Go (Golang)

About this course

Build a Production-Style RAG System in Go — From Zero to Streaming Chat

Learn Retrieval-Augmented Generation by building one yourself, in plain Go, against any OpenAI-compatible model — local or hosted.

Stop reading about RAG and start shipping it. In this hands-on course you will build a complete, end-to-end Retrieval-Augmented Generation system from the ground up using the Go programming language. No Python. No LangChain. No magical abstractions. Just clear, idiomatic Go code that you can read, modify, and own.

By the end of the course, you will have a working application featuring a streaming terminal chat REPL, a browser-based chat UI with token-by-token Server-Sent Events, file and image uploads, a background filesystem watcher that ingests documents automatically, an evaluation harness that scores retrieval quality, and a Postgres + pgvector backend running in Docker.

Why this course?

Most RAG tutorials hide the interesting parts behind a framework. You wire three lines of someone else's library together, it works, and you have no idea what just happened. When something breaks in production — and it will — you are stuck.

This course takes the opposite approach. Every component is built explicitly, with clean seams between concepts so you can see exactly where the LLM client ends and the vector store begins. The package layout maps directly to lecture chapters. The interfaces between the LLM, the embedder, the vector store, the retriever, the chat loop, and the web server are deliberately exposed so you can swap pieces in and out as exercises.

This is the course I wish existed when I was learning RAG.What you will buildA small but real RAG application with all the moving parts of a production system:

• A streaming chat REPL (Read-Eval-Print Loop) in the terminal with a "thinking" spinner and proper

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