Quick Labs

Build a RAG App in 1 Hour

Hands-on lab: load documents, embed them, store in a vector DB, and wire an LLM to retrieve and answer. End with a working prototype you can keep iterating on.

1 Hour

Instructor

Javed

What you'll learn

How a RAG pipeline works end-to-end

Chunking and embedding documents

Storing and querying vectors in a vector DB

Connecting an LLM to a retrieval layer

Evaluating RAG output quality

Requirements

Basic Python familiarity
A laptop with internet access
Free OpenAI / Anthropic API key

About this course

A focused 1-hour sprint where you build a retrieval-augmented generation (RAG) application end-to-end. You'll chunk and embed your own documents, push them into a vector store, and connect an LLM that retrieves the right context before answering. By the end of the session, you'll leave with a working prototype, the code, and a clear mental model of how RAG pipelines work in production.

Course curriculum

5 lessons

0–10 min: Setting up the environment & API keys
10–25 min: Chunking and embedding your documents
25–40 min: Vector store + retrieval
40–55 min: Wiring the LLM, prompting & answering
55–60 min: Evaluating output and next steps

Your instructor

Javed

Former Research Scientist, NASA

4.8 instructor rating

Javed is a former NASA research scientist with deep experience translating research-grade ML into production systems. He has built and shipped LLM-powered tools across research, climate, and aerospace contexts.