RAG Foundations Byte

Understand how Retrieval-Augmented Generation bridges the gap between static LLM knowledge and real-time private data.

Abstract Algorithms

Jul 2, 2026·1 min read·Intermediate

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Quick Take

Retrieval-Augmented Generation (RAG) feeds relevant external documents into an LLM's prompt context before generating a response. This prevents hallucinations and bypasses static model training limits

Retrieval-Augmented Generation (RAG) feeds relevant external documents into an LLM's prompt context before generating a response.

This prevents hallucinations and bypasses static model training limits.

📊 RAG Architecture

User Query ──┬──► [ Vector Store Search ]
             │            │
             │      (Retrieves Context Documents)
             ▼            ▼
         [ Formatted Context Prompt ] ──► [ LLM Generation ] ──► Response

Ingestion: Split documents into chunks, convert them to vector embeddings, and store them in a vector database.
Retrieval: Use similarity search (like Cosine distance) to find vector chunks closest to the user's query.
Generation: Combine the user query and retrieved document context into a prompt, allowing the LLM to write an accurate answer grounded in your documents.

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