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follow @sambit.ai.tech LLM vs RAG vs AI AGENT vs AGENTIC AI What's the Difference & When to Use Each? LLM Large Language Model The Thinker Reasons with language RAG Retrieval-Augmented Generation The Researcher Finds answers in your knowledge base AI AGENT The Doer Completes tasks using tools AGENTIC AI The Coordinator Orchestrates multiple agents USE IT WHEN You need writing, rewriting, summarising or general Q&A. The answer sits in your internal documents, policies or knowledge base. A defined task needs completing end to end. A workflow spans teams, systems or agents and needs coordination. STRENGTHS Fast, simple, cheapest to deploy. Works out of the box without any custom data. Finds answers in your actual data. Reduces hallucination with source references. Acts, not just answers: creates, updates, sends. Handles multi-step tasks within clear limits. Coordinates multiple agents toward a goal. Adapts when conditions change. WATCH OUT FOR No access to your internal data. Hallucination risk on specific facts. Needs a clean knowledge base. Missed retrieval can still cause wrong answers. Needs clear task boundaries and tool definitions. Errors may compound across steps. Hardest to design, monitor and audit. Compounding errors across agents. EXAMPLE Drafting emails, summarising meetings or answering general questions. An HR chatbot that searches the employee handbook. A support agent that checks an order, shipping status and drafts a reply. An incident response system where agents detect, triage, notify and draft comms. RAG uses your data to give accurate, up-to-date answers.