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.
Key Concepts:
Everything 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:
A 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.
This is the core engine underneath everything people refer to as Gen AI.
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.
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.
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.
What RAG actually does:
RAG 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.
What this layer provides:
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.
How it works:
None 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.
Once the stack is visible layer by layer, the next AI acronym is just one more narrow problem being solved — not a new mystery.
If you found this useful, feel free to share it with someone still trying to untangle the acronym soup. 😊
AI Basics: LLM, RAG, MCP and Agentic AI Explained in simple words was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.