What is the difference between RAG and fine-tuning?

RAG fetches information at the moment of the question and leaves the model unchanged. Fine-tuning retrains the model on new examples so the knowledge lives inside its weights. RAG suits large, changing knowledge bases and keeps a citation trail; fine-tuning suits changing how the model behaves, its tone, format, or reasoning style, rather than which facts it knows.

What is the difference between RAG and an MCP server?

They solve different problems and are often used together. RAG is a technique for grounding an answer in retrieved passages. MCP is a protocol that lets a model discover and call tools and data sources. A system can use MCP to reach a database or search index and then apply RAG to the results. CorpusIQ uses MCP to reach business systems and returns source-cited answers.

Does RAG stop hallucinations?

It reduces them; it does not eliminate them. Grounding the model in real passages makes a wrong answer far less likely, but the model can still misread a passage, overstate what it says, or stitch sources together incorrectly. That is why citations matter: they turn every claim into something you can check against the original record.

Do I need a vector database to use RAG?

No. The retriever can be a keyword search, a SQL query, or a full-text index; a vector database is one option, not a requirement. Vector search helps when the match is about meaning rather than exact words. What matters is that the right passages reach the model, however they are found.

Answers

What is RAG? Retrieval-Augmented Generation, Explained

RAG stands for retrieval-augmented generation. It is a way to build AI answers that look up real information before they respond, instead of relying only on what the model memorized during training. When a question arrives, a retriever pulls the most relevant passages from a knowledge source, the model is given those passages alongside the question, and it writes an answer grounded in that retrieved material. The answer can name where each claim came from, which makes it checkable in a way a purely memorized answer is not.

Related pillar: Inside the intelligence layer

What RAG stands for and what it does

The acronym spells out the sequence. Retrieval means the system finds relevant information in an outside source, a document store, a database, or a search index. Augmentation means that retrieved material is added to the model's input along with the question, so the model is no longer answering from memory alone. Generation means the model writes the response from that combined input. The retrieval step is what separates a RAG answer from a plain language-model answer: the model generates from evidence, not just from probability.

The parts of a RAG system

A working RAG system has three moving parts. A store holds the knowledge, usually a set of documents split into chunks, with each chunk turned into an embedding, a numeric representation that captures meaning. A retriever takes the question, converts it to the same embedding space, and finds the nearest matching chunks. A generator, an LLM, receives those chunks plus the question and produces the final answer. None of these parts is exotic; what makes the difference is how well the retrieved chunks actually answer the question.

Why retrieval beats answering from memory

A model's training data is generic and frozen at the moment it was trained. It cannot know this month's numbers, your documents, or anything published after its cutoff. It also cannot show its work. Retrieval fixes both problems at once: the answer is grounded in material that can be current and specific to the user, and each sentence can point at the exact source it came from. That citation is the difference between taking an answer on faith and checking it.

RAG versus fine-tuning and long context windows

There are three ways to give a model knowledge it was not trained on. Fine-tuning bakes new examples into the model's weights, which changes the model and costs a training run. A long context window loads everything into the prompt, which gets expensive and slow as the context grows. RAG fetches only what is relevant at question time, so the knowledge can change without retraining and the prompt stays small. RAG wins when the knowledge is large and changes often; fine-tuning wins when the style or behavior needs to change, not the facts.

Where RAG already runs

Document question-and-answer assistants, customer support bots, code assistants that search a repository, and enterprise search over files and email are all RAG under the hood. CorpusIQ is the same pattern applied to business data: it reads the systems you approve at question time through 40+ connectors, then the assistant answers with a citation on each figure. You can see the underlying record, so an answer is a claim you can check, not a statement you have to trust.

Try CorpusIQ on your business

40+ read-only connectors. ChatGPT, Claude, and Perplexity. Solo $29.95 a month. 30-day free trial.

Start free trial Inside the intelligence layer

What is RAG? Retrieval-Augmented Generation, Explained

What RAG stands for and what it does

The parts of a RAG system

Why retrieval beats answering from memory

RAG versus fine-tuning and long context windows

Where RAG already runs

What is RAG? Retrieval-Augmented Generation, Explained

What RAG stands for and what it does

The parts of a RAG system

Why retrieval beats answering from memory

RAG versus fine-tuning and long context windows

Where RAG already runs

Related answers

Try CorpusIQ on your business

What is RAG? Retrieval-Augmented Generation, Explained

What RAG stands for and what it does

The parts of a RAG system

Why retrieval beats answering from memory

RAG versus fine-tuning and long context windows

Where RAG already runs

Related answers

Try CorpusIQ on your business