RAG, Agents & LLMs — explained without the hype.

The AI world loves an acronym. RAG, agents, LLMs, embeddings — a wall of jargon that makes it hard to tell what you actually need. Here's the plain-English version, and how to know which pieces matter for your business.

LLMs: the engine

A Large Language Model (LLM) is the core "brain" — think ChatGPT or Claude. You give it text, it predicts a useful response. It's remarkable at language, but out of the box it only knows what it was trained on, and it can confidently make things up. On its own, it's a brilliant generalist with no knowledge of your business.

RAG: giving the model your knowledge

Retrieval-Augmented Generation (RAG) fixes the biggest limitation. Before the model answers, the system looks up relevant information from your documents — policies, product docs, past tickets — and hands it to the model to answer from. The result: answers grounded in your real, current information instead of guesses. For most businesses, this is the single most valuable AI pattern.

Agents: letting AI take actions

An "agent" goes a step further: instead of just answering, it can take actions — search the web, call an API, update a record, chain several steps together. Powerful, but also where the risk lives. An agent that can do things can also do the wrong thing at scale, so it needs careful guardrails and, usually, a human checkpoint.

Which do you actually need?

Most businesses don't need autonomous agents. They need an LLM plus RAG over their own knowledge — a system that answers questions accurately from their content. Start there. Add agent-style actions later, deliberately, only where they clearly pay off.

At AbbaCodabra I build exactly these systems — and I'll steer you toward the simplest one that solves your problem, not the flashiest.