Brain vs fresh knowledge vs the decision-maker: how an agent plans and acts using an LLM plus tools and retrieval (PDF 181–182, shared with the next topic).
Agent vs LLM vs RAG
Let’s break it down with a simple analogy:
● LLM is the brain.
● RAG is feeding that brain with fresh information.
● An agent is the decision-maker that plans and acts using the brain and the tools. LLM (Large Language Model) An LLM like GPT-4 is trained on massive text data. It can reason, generate, summarize but only using what it already knows (i.e., its training data).
It’s smart, but static. It can’t access the web, call APIs, or fetch new facts on its own.
RAG (Retrieval-Augmented Generation) RAG enhances an LLM by retrieving external documents (from a vector DB, search engine, etc.) and feeding them into the LLM as context before generating a response.
RAG makes the LLM aware of updated, relevant info without retraining. Agent An Agent adds autonomy to the mix.
It doesn’t just answer a question—it decides what steps to take: Should it call a tool? Search the web? Summarize? Store info? An Agent uses an LLM, calls tools, makes decisions, and orchestrates workflows just like a real assistant.