This glossary covers Retrieval-Augmented Generation (RAG), a method that blends search with AI to deliver grounded answers. We break down how it works, why it matters, and the different ways it can be applied—all in a clear, straightforward way.

What is Retrieval-Augmented Generation?

RAG, short for Retrieval-Augmented Generation, is a way of making AI smarter by letting it pull in outside information while it writes. Instead of only relying on what the model “remembers,” it searches through documents, databases, or other sources and uses those results to shape its answer. That means the response is more accurate, up to date, and backed by real evidence.

How Does Retrieval-Augmented Generation Work?

It is easier to picture if you think about how people answer tough questions. Most of us don’t just rely on memory—we look something up, skim a few pages, and then explain it in our own words. That’s basically what’s happening here. The system slices your content into smaller bits so it can search through them quickly. When a question comes in, it grabs a handful of those bits that look useful and passes them along with the question to the AI. The AI then writes an answer that mixes the person’s request with the pieces it just pulled. It’s less about magic, more about doing your homework before you speak.

Key Features

This method is powerful because it makes answers more reliable and explainable. You can see where the information came from, and you can choose how the AI searches—whether that’s through keyword matches, semantic similarity, or a mix of both. On top of that, there are advanced setups that let the system double-check itself, run follow-up searches, or even use structured knowledge like graphs for more complex questions.

Benefits

The main win is speed and accuracy without the hassle of retraining a whole model whenever your data changes. It’s cheaper than fine-tuning, and because it can cite sources, users are more likely to trust the answers. Plus, it works across industries—from support desks to research teams—so it’s flexible no matter what type of data you’re dealing with.

Use Cases

RAG shows up in a lot of practical places. Companies use it to answer employee questions, power customer service bots, and help researchers sift through piles of information. It’s also great for industries where compliance matters, since every answer can link back to a source. And for tougher tasks, it can combine information across documents to give a bigger-picture view.

Types of Retrieval-Augmented Generation

Here are the main flavors, each with its own strength:

Single-shot RAG

Retrieves once, answers once. Simple and fast, good for FAQs or straightforward queries.

RAG-Sequence / RAG-Token

Variants from the original paper; they decide how and when passages are pulled in during the answer. Useful when fine-grained grounding matters.

Hybrid RAG

Mixes vector search (semantic meaning) and keyword search. Best when your data is messy, technical, or full of unique names.

RAG-Fusion / Query Expansion

Runs multiple versions of the question and combines results. Great when the question is broad or unclear.

GraphRAG

Builds a knowledge graph out of your data and retrieves from it. Perfect for “big picture” or multi-step reasoning.

Agentic RAG

Adds an extra loop where the AI checks its own answer, searches again if needed, and refines the response. Good for complex or high-stakes use cases.

Multimodal RAG

Goes beyond text to include images, charts, or other media. Handy when the answer depends on visual content.

How to Choose the Right One

Which setup you choose depends on the job. If you’re looking for quick, simple answers, single-shot would be sufficient. In case of scattered or technical content, choose hybrid for a balance of precision and recall. When you need global insights or connections, graph-based is the way to go. If accuracy is critical, agentic gives you that extra safety net. And if you’re working with visuals, multimodal is the clear choice.