RAG Learning Journey Part 1: What Is Retrieval‑Augmented Generation?
This article explains Retrieval‑Augmented Generation (RAG), describing its definition, why it complements large language models, the end‑to‑end workflow, its four core components—embedding, vector database, retriever, generator—and the value it adds by providing up‑to‑date knowledge and reducing model hallucinations.
What Is RAG?
Retrieval‑Augmented Generation (RAG) is an information‑retrieval‑enhanced generation technique that works with large language models to improve answer relevance by incorporating external knowledge.
Why Combine With Large Models?
Large models use cosine similarity to compare text embeddings, which speeds up query processing.
RAG supplies up‑to‑date context that the model’s static training data lacks, thereby increasing effective generation efficiency.
Overall RAG Workflow
The pipeline consists of document embedding, vector storage, similarity search, and generation. An illustration shows the flow: document vectorization → semantic similarity comparison → augmented generation.
Core Components
Knowledge Embedding : converts text into vector representations.
Vector Database : stores the embeddings.
Retriever : transforms a user query into a vector and retrieves similar document fragments.
Generator : produces answers using the retrieved context.
Value of RAG
RAG enables dynamic incorporation of the latest domain‑specific knowledge, reducing hallucinations and outdated information in large language model outputs.
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