Intermediate In Progress

Build a RAG-Powered AI Chatbot

Create a retrieval-augmented generation chatbot with embeddings, vector search, and LLM integration.

Start Module 1 Browse modules

At a glance

Duration: 12-16 hours
Modules: 2 total · 2 live
Progress: 10%

Tech stack

Docker GCP

Project Modules

Follow these build guides in order. Published modules are ready to start.

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1 Module 1: RAG Architecture & Document Chunking Design a RAG pipeline and implement intelligent document chunking strategies. Published 2 Module 2: Vector Embeddings & Similarity Search Generate embeddings with OpenAI or sentence-transformers and store them in a vector database. Published

Before You Start

Python programming
Basic ML/NLP concepts

What You'll Learn

✓Design RAG architecture
✓Chunk and embed documents
✓Implement vector similarity search
✓Integrate LLM for grounded responses
✓Evaluate answer quality

Implementation Roadmap

Document Ingestion

Load, chunk, and preprocess knowledge base documents.

Active

Embeddings & Vector Store

Generate embeddings and store in ChromaDB or Pinecone.

Planned

LLM Integration & UI

Wire retrieval to an LLM and build a chat interface.

Planned