{"slug": "ai-basics-llm-rag-mcp-and-agentic-ai-explained-in-simple-words", "title": "AI Basics: LLM, RAG, MCP and Agentic AI Explained in simple words", "summary": "Towards AI published a guide explaining the AI technology stack, covering Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and Agentic AI. The article clarifies that LLMs are scaled deep learning models for language generation, RAG adds external data retrieval without retraining, MCP enables tool integration, and Agentic AI uses an orchestrator to delegate tasks to specialized agents. It emphasizes that each layer builds on the previous one, with LLMs as the core engine.", "body_md": "AI has turned into an acronym soup — LLM, GenAI, RAG, MCP, Agentic AI — and most people nod along without really knowing where one ends and the next begins. This post breaks the stack down layer by layer, so the next acronym that shows up doesn’t feel like a mystery.\n\n**Key Concepts:**\n\nEverything in this stack traces back to deep learning — neural networks trained on large volumes of data to recognise patterns. What looks like separate technologies is usually the same underlying technique, just pointed at different kinds of data:\n\nA **Large Language Model** is what you get when you scale the text branch of deep learning up dramatically. More parameters, more training data, and the result is a model that doesn’t just recognise language patterns — it can generate fluent, contextual language in response to a prompt.\n\nThis is the core engine underneath everything people refer to as Gen AI.\n\n**Generative AI**, stripped of the marketing language, is essentially a very capable summarization and pattern-completion system. Whether it’s writing an email, generating code, or answering a question, the model is compressing what it has learned into an output relevant to your specific prompt.\n\n**Limitation:** An LLM only knows what it was trained on. It has no visibility into your company’s internal documents, real-time data, or anything created after its training cutoff.\n\n**Retrieval-Augmented Generation (RAG)** addresses that gap without retraining the model. At query time, relevant documents or data are retrieved and passed to the LLM as context before it generates a response.\n\n**What RAG actually does:**\n\nRAG solves retrieval of information, but real work often needs more than reading documents — checking a calendar, querying a live database, or triggering an action in another system. This is where **MCP (Model Context Protocol)** and tool-calling come in.\n\n**What this layer provides:**\n\n**Agentic AI** is a system design pattern, not a single more powerful model. An **orchestrator** breaks a task into sub-tasks and delegates each to a **specialised worker agent** — a coding agent, a research agent, or a data-analysis agent — each with narrow, deep expertise in one domain.\n\n**How it works:**\n\nNone of these layers replace the ones below them — each extends the last. An agentic system without RAG has nothing grounded to work from. RAG without tools can read but can’t act. Tools without an orchestrator are just disconnected capabilities. And none of it functions without an LLM’s core ability to generate coherent language in the first place.\n\nOnce the stack is visible layer by layer, the next AI acronym is just one more narrow problem being solved — not a new mystery.\n\n*If you found this useful, feel free to share it with someone still trying to untangle the acronym soup. 😊*\n\n[AI Basics: LLM, RAG, MCP and Agentic AI Explained in simple words](https://pub.towardsai.net/ai-basics-llm-rag-mcp-and-agentic-ai-explained-in-simple-words-d38dd36bf8ac) was originally published in [Towards AI](https://pub.towardsai.net) on Medium, where people are continuing the conversation by highlighting and responding to this story.", "url": "https://wpnews.pro/news/ai-basics-llm-rag-mcp-and-agentic-ai-explained-in-simple-words", "canonical_source": "https://pub.towardsai.net/ai-basics-llm-rag-mcp-and-agentic-ai-explained-in-simple-words-d38dd36bf8ac?source=rss----98111c9905da---4", "published_at": "2026-08-26 04:37:19+00:00", "updated_at": "2026-08-26 04:42:59.303073+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "generative-ai", "ai-agents"], "entities": ["Towards AI", "Large Language Model", "Generative AI", "Retrieval-Augmented Generation", "Model Context Protocol", "Agentic AI"], "alternates": {"html": "https://wpnews.pro/news/ai-basics-llm-rag-mcp-and-agentic-ai-explained-in-simple-words", "markdown": "https://wpnews.pro/news/ai-basics-llm-rag-mcp-and-agentic-ai-explained-in-simple-words.md", "text": "https://wpnews.pro/news/ai-basics-llm-rag-mcp-and-agentic-ai-explained-in-simple-words.txt", "jsonld": "https://wpnews.pro/news/ai-basics-llm-rag-mcp-and-agentic-ai-explained-in-simple-words.jsonld"}}