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RETRIEVAL ~15 min6 concepts · 5 quiz questions

Production RAG Systems

Hybrid RAG, GraphRAG, Multimodal RAG, Agentic RAG, Caching, Guardrails & Evaluation.

Overview

Retrieval-Augmented Generation (RAG) connects LLMs to external knowledge. This module covers the evolution from naive RAG to production-grade systems with hybrid search, graph-based retrieval, multimodal pipelines, and robust evaluation frameworks that measure retrieval quality separately from generation quality.

Concept Flashcards

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Concept

Naive RAG vs. Advanced RAG

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Explanation

Naive RAG: chunk → embed → retrieve → generate. Advanced RAG adds query rewriting, re-ranking, parent-document retrieval, and iterative refinement. HyDE (Hypothetical Document Embeddings) generates a fake answer to use as a better query vector.

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System Architecture

4 interactive diagrams — drag nodes · scroll to zoom · click for details

The simplest RAG architecture: chunk documents, embed them, store in a vector DB, retrieve the top-k, and pass as context to the LLM.

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storeANN searchtop-k contextuser promptgenerateDocumentsChunkerEmbedderVector DBUser QueryTop-K RetrieveLLMAnswer
Data Source
Embedding
Vector Store
LLM
User I/O

Key Concepts

6 concepts — click to expand

Naive RAG: chunk → embed → retrieve → generate. Advanced RAG adds query rewriting, re-ranking, parent-document retrieval, and iterative refinement. HyDE (Hypothetical Document Embeddings) generates a fake answer to use as a better query vector.

Tech Stack

LangChainLlamaIndexQdrantFAISSOpenSearchWeaviateRAGASCohere
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