← Concepts

Building RAG Systems

Learn to ground LLMs in external knowledge using retrieval, chunking, and reranking.

Concepts 6
Total time ~57 min
Interactive 2
  1. Step 1

    Embeddings & Semantic Search

    Interactive

    Learn how embeddings turn text into vectors and enable semantic search by finding meaning-based similarity instead of keyword matches.

    intermediate 9 min read
  2. Step 2

    Vector Databases & Approximate Nearest Neighbors (ANN)

    Interactive

    Learn 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
  3. Step 3

    Retrieval-Augmented Generation (RAG)

    Learn how RAG lets an LLM answer questions using relevant external documents fetched at query time.

    intermediate 8 min read
  4. Step 4

    Chunking & Indexing Strategies for RAG

    Learn 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
  5. Step 5

    Reranking & Hybrid Retrieval

    Learn why two-stage retrieval and keyword+vector fusion improve relevance in real-world RAG systems.

    intermediate 10 min read
  6. Step 6

    Context Windows & Prompt Budgeting

    Build a practical mental model for context limits and how to allocate tokens for better cost, speed, and answer quality.

    intermediate 9 min read

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Customizing Models

From prompt engineering to fine-tuning to building autonomous agents.

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