📖Lessons
Introduction to RAG
Learn what Retrieval-Augmented Generation is and why it's essential for AI applications
Vector Databases Basics
Understand vector databases and how they enable semantic search in RAG systems
Embeddings Explained
Learn how text is converted into vectors and how to choose the right embedding model
Chunking Strategies
Learn how to split documents effectively for optimal RAG retrieval performance
Similarity Search Techniques
Master different search methods to find the most relevant chunks in your RAG system
Retrieval Optimization
Advanced techniques to improve RAG retrieval quality and reduce hallucinations
Reranking Strategies
Learn advanced reranking techniques to improve the relevance of retrieved results
Workshop: Building Your First RAG System
Build a complete RAG system from scratch with document indexing and question answering
RAG Evaluation and Testing
Learn how to systematically measure and improve your RAG system's quality
Production RAG Systems
Deploy, scale, and maintain RAG systems in production environments
🎯Missions
Fix the Embedding Service
A junior developer at Nebula Corp submitted a PR for the embedding service, but it has several bugs. Review the code, identify the issues, and fix them before this goes to production.
Optimize RAG Pipeline Costs
Nebula Corp's RAG pipeline is burning through API credits. The current implementation sends full documents to the LLM for every query. Refactor the pipeline to reduce cost while maintaining answer quality above the threshold.
🔧Workshops
Create a Custom Reranker
Build a reranker that improves RAG accuracy from 60% to 85% using cross-encoders.
Implement Semantic Caching
Build a semantic cache that reduces API costs by 70% using embedding similarity.
Metadata Extraction Pipeline
Build a pipeline to automatically extract and enrich metadata from documents using LLMs.
RAG Debugging Tool
Create a debugger that visualizes retrieval results, shows chunk overlap, and identifies gaps.