LLM + RAG
The LLM + RAG architecture is a methodology designed to enhance the factual accuracy and relevance of a Large Language Model's output, especially when dealing with private, real-time, or domain-specific information that was not included in its original training data.
The Two Components
1. LLM (Large Language Model)
The LLM serves as the reasoning and generation engine. It is highly capable of understanding complex human language, reasoning through instructions, and generating coherent, grammatically correct text. However, its knowledge is static, limited to its training cutoff date, and sometimes prone to hallucinations (generating plausible-sounding but false information).
2. RAG (Retrieval-Augmented Generation)
RAG is the factual grounding mechanism that mitigates the LLM's limitations:
Retrieval: When a user asks a question, the RAG system first searches an external, trusted knowledge base (e.g., a company's financial documents, an IT support manual, or a live API). It retrieves the top most relevant document snippets (or "chunks").
Augmentation: The system then takes these retrieved snippets and inserts them directly into the prompt given to the LLM. The prompt essentially tells the LLM: “Answer the user’s question, but you must use these specific provided facts.”
Generation: The LLM uses the fresh, retrieved context to generate a precise, factual answer, often citing the source document.
Benefits of RAG
Reduces Hallucination: Forces the LLM to rely on verified sources rather than its memorized, outdated, or invented training data.
Enables Custom Knowledge: Allows the LLM to access and utilize a company's private, proprietary data without retraining the entire massive model.
Transparency: Provides the user with source citations, allowing them to verify the information.
