📖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
M-034Build RAG Evaluation Suite
CloudDocs Inc deployed a RAG system but has no way to measure quality. Users complain about irrelevant answers, but there's no data to guide improvements. Build an evaluation suite with test queries, ground truth answers, and automated metrics (precision, recall, MRR).
M-037Build Two-Stage Retrieval with Reranking
LegalTech AI's RAG system retrieves 10 documents but only 3 are relevant (precision@10 = 0.30). The bi-encoder is fast but imprecise. Implement a two-stage pipeline: fast bi-encoder retrieval followed by cross-encoder reranking to improve precision.
M-029Build Your First Document Q&A System
Nebula Corp has a collection of product FAQ documents but no way to search them intelligently. Users type questions and get nothing useful back. Build a basic RAG pipeline: embed the documents, find the most relevant ones for a user query using cosine similarity, and construct a prompt that includes the retrieved context so the LLM can generate a grounded answer.
M-026Build Your First Similarity Search
Nebula Corp has a knowledge base of product descriptions stored as embedding vectors, but no way to search them. Build a similarity search function that takes a query vector, compares it against all stored document vectors using cosine similarity, and returns the top-K most relevant results ranked by score.
M-031Compare Embedding Models for Domain-Specific RAG
MedTech AI's RAG system uses a general-purpose embedding model (MiniLM) but struggles with medical terminology. 'myocardial infarction' and 'heart attack' aren't recognized as similar. Test different embedding models and measure which performs best on medical queries.
M-028Fix 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.
M-032Implement Hybrid Search for Better Accuracy
TechDocs Inc's RAG system misses exact keyword matches. A query for 'ERR_CONNECTION_REFUSED' returns generic networking docs instead of the specific error code documentation. Implement hybrid search combining semantic and keyword search to improve precision.
M-027Implement Metadata Filtering for Multi-Tenant RAG
SecureDoc's RAG system has a critical security bug: users can see documents from other organizations! The vector database returns results from all tenants. Implement metadata filtering to ensure users only retrieve documents they have access to.
M-036Implement Query Decomposition for Complex Questions
AnalyticsPro's RAG system fails on multi-part questions. A query like 'Compare pricing between Pro and Enterprise plans and explain which includes API access' returns incomplete answers. Implement query decomposition to break complex questions into focused sub-queries.
M-030Optimize Chunking Strategy for Better Retrieval
DataFlow Inc's RAG system has poor retrieval accuracy (precision@5 = 0.45). The current fixed-size chunking splits documents mid-sentence, breaking context. Implement and test different chunking strategies to improve retrieval quality above the target threshold.
M-033Optimize Context Window Packing for RAG
Nebula Corp's RAG system retrieves 10 chunks but naively concatenates them all, often exceeding the LLM's context window and getting truncated. Important information at the end gets cut off. Implement a smart context packer that: estimates token counts, prioritizes the most relevant chunks, and fits as much high-quality context as possible within the token budget — without exceeding it.
M-035Optimize 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
W-012Create a Custom Reranker
Build a reranker that improves RAG accuracy from 60% to 85% using cross-encoders.
W-011Implement Semantic Caching
Build a semantic cache that reduces API costs by 70% using embedding similarity.
W-009Metadata Extraction Pipeline
Build a pipeline to automatically extract and enrich metadata from documents using LLMs.
W-010RAG Debugging Tool
Create a debugger that visualizes retrieval results, shows chunk overlap, and identifies gaps.