IntermediateFeatured
Production RAG
Build retrieval systems that actually work in production
End-to-end production RAG: document ingestion, chunking, embeddings, vector search, retrieval, reranking, evaluation and production architecture. From naive chatbot to reliable knowledge system.
Skills you'll gain
RAGEmbeddingsVector SearchChunkingRerankingEvaluationProduction Architecture
Prerequisites
LLM APIsPython
Chapters
1
Why RAG?Intermediate
Why retrieval-augmented generation beats fine-tuning and long-context for grounding LLMs in your data.
18m
2
Document IngestionIntermediate
Build a robust ingestion pipeline: multi-format extraction, cleaning, metadata, and idempotent updates.
24m
3
Chunking StrategiesIntermediate
Fixed, recursive, semantic and section-aware chunking. The #1 lever for RAG quality.
28m
4
Retrieval & RerankingAdvanced
Hybrid search, reranking, and the production retrieval stack that produces precise context.
32m
Labs in this series
Document Chunking Strategies
IntermediateImplement three chunking strategies and measure their impact on a sample retrieval eval.
60m3 skills
Embeddings & Vector Search
IntermediateImplement a VectorStore with embed, insert, and search(filter) methods.
55m4 skills
Reranking for Retrieval Precision
AdvancedRetrieve top-20 with vector search, rerank to top-5, and measure precision@5 improvement.
50m3 skills