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Build Your First RAG App (placeholder)

Go from a folder of PDFs to a cited, production-ready AI assistant.

PLACEHOLDER. A hands-on course where you build a retrieval-augmented generation app end to end: ingestion, embeddings, vector search, prompting with citations and evaluation.

Level
Intermediate
Duration
6 hours
Modules
3
Lessons
11
Documents flowing into a vector index and out as a cited answer

Outcomes

What you'll learn

  • Chunk and embed documents the right way for your data
  • Store and query vectors with Postgres + pgvector
  • Write prompts that answer with citations and admit when they don't know
  • Measure answer quality with a simple evaluation set
  • Deploy the app on Vercel

About this course

Placeholder description. Replace this file with your real course description, or delete it.

Most RAG tutorials stop at a demo that works on three documents. This course goes further. You'll build an assistant that handles real, messy documents, answers with citations you can click, and tells you when it doesn't know.

How the course works

Each module ends with a working milestone, so you always have something running:

  1. Foundations. Understand embeddings and set up the project.
  2. Ingestion and retrieval. Turn PDFs into searchable chunks and find the right ones.
  3. Generation and shipping. Write grounded prompts, stream answers and deploy.

What you'll build

A "chat with your docs" app in Next.js with Postgres + pgvector, streaming answers, source citations and a small evaluation suite you can run before every release.

Curriculum

3 modules · 11 lessons · 6 hours

  1. 1Foundations3 lessons · 30 min
    1. What RAG is (and isn't) Preview8 min
    2. Embeddings in plain English Preview12 min
    3. Project setup10 min
  2. 2Ingestion and retrieval4 lessons · 1h 15m
    1. Loading and cleaning documents15 min
    2. Chunking strategies compared18 min
    3. Vector search with pgvector20 min
    4. Hybrid search and re-ranking22 min
  3. 3Generation and shipping4 lessons · 1h 8m
    1. Prompting with citations16 min
    2. Streaming answers in Next.js18 min
    3. Evaluating answer quality20 min
    4. Deploying to production14 min

Is it for you?

Who this course is for

  • Web developers who want to add AI features to real products
  • Engineers who tried a RAG demo and want to make it reliable
  • Students who know basic TypeScript or Python

Prerequisites

  • Comfortable with JavaScript or TypeScript
  • Basic SQL

Get notified when it launches

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