Building RAG Systems
Learn to ground LLMs in external knowledge using retrieval, chunking, and reranking.
Some steps reference prerequisites outside this path. Use the prep links inside those steps before continuing.
- Step 1
Embeddings & Semantic Search
CompletedInteractiveLearn how embeddings turn text into vectors and enable semantic search by finding meaning-based similarity instead of keyword matches.
intermediate 9 min readPrep recommended: Tokenization - Step 2
Vector Databases & Approximate Nearest Neighbors (ANN)
CompletedInteractiveLearn what vector databases store, why nearest-neighbor search must be approximate at scale, and how ANN indexes (like HNSW and IVF) make retrieval fast.
intermediate 10 min read - Step 3
Retrieval-Augmented Generation (RAG)
CompletedLearn how RAG lets an LLM answer questions using relevant external documents fetched at query time.
intermediate 8 min read - Step 4
Chunking & Indexing Strategies for RAG
CompletedInteractiveLearn how to split documents into retrievable chunks, attach the right metadata, and index content so RAG retrieves the right context reliably.
intermediate 11 min read - Step 5
Reranking & Hybrid Retrieval
CompletedInteractiveLearn why two-stage retrieval and keyword+vector fusion improve relevance in real-world RAG systems.
intermediate 10 min read - Step 6
Context Windows & Prompt Budgeting
CompletedInteractiveBuild a practical mental model for context limits and how to allocate tokens for better cost, speed, and answer quality.
intermediate 9 min read