AI System Architecture: Build a RAG Chatbot

This premier offering moves beyond using models to mastering their architecture, culminating in a production-ready Retrieval-Augmented Generation (RAG) system. A true portfolio differentiator.

Level

Advanced

For

Grades 9-12

Duration

3 or 5 Days

What You Will Master

Transformer Architecture

Understand the fundamental components of modern LLMs, including attention mechanisms.

Semantic Search & Vector Databases

Master semantic search and get hands-on with vector databases like ChromaDB for efficient retrieval.

RAG Pipeline Implementation

Implement a complete Retrieval-Augmented Generation pipeline from document ingestion to response.

Full-Stack AI with Next.js & Genkit

Create a full-stack web application to serve your custom AI model to the world.

The Capstone Project

Specialized Q&A Chatbot

A highly impressive and modern portfolio project. Students will build a full-stack chatbot that answers questions accurately based on a custom corpus of documents (e.g., a specific textbook, financial reports). The RAG architecture eliminates hallucinations and provides cited sources, a key feature of enterprise-grade AI systems.

Key Transformation

Build a production-ready, specialized Q&A chatbot using Retrieval-Augmented Generation (RAG) to provide accurate, source-based answers, demonstrating mastery of modern LLM systems.

Course Syllabus

1
Session 1: Deep Dive into LLM Architecture

Move beyond the API. Understand the Transformer, attention mechanisms, and the power of embeddings that give language models their sense of "meaning".

2
Session 2: Embeddings & Vector Search

Learn how to create and store embeddings from your documents for efficient, meaning-based semantic search using a vector database. Implement document chunking strategies.

3
Session 3: Building the RAG Pipeline

Combine a vector database with an LLM to create a system that retrieves relevant information and uses it as context to generate an informed, accurate answer. Handle prompt construction and context management.

4
Session 4: Full-Stack Integration & Deployment

Build a web interface for your chatbot using Next.js and connect it to your Genkit AI backend, deploying a complete, shareable application on Firebase.

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Build Your Advantage

Our project-based workshops are designed to give you a tangible, verifiable edge. Enroll now to secure your spot and start building your future.