RAG Explained: How to Stop AI From Making Things Up
AI doesn’t know your company’s documents, so when asked about them, it makes things up, and the answer looks just as convincing as a real one. Here’s how retrieval-augmented generation (RAG) lets AI work with your actual business knowledge, and what to look for when choosing a solution.
TL;DR
An AI tool asked about internal company documents it has never seen will hallucinate, producing a plausible-sounding answer with no basis in fact. RAG (retrieval-augmented generation) addresses this by having the system search a company knowledge base first and build its response from the retrieved content. Hallucinations drop significantly, though they don’t disappear entirely, because errors can still creep in through outdated or poorly prepared source material. Whether you can trust the AI’s answers ultimately comes down to how the solution is implemented.
AI only knows what it was trained on. That sounds obvious, but how many of us actually think about what it means? AI tools are everywhere now, and most people reach for them reflexively, drawn by the convenience. It’s worth remembering, though, that these are still just tools, and they have limits. That matters especially in a business setting.
A model that was never exposed to your company’s documents and data knows nothing about them. Ask it anyway, and it may still produce an answer, because that’s what it’s built to do. That answer will be a guess, an attempt to fulfill the request with whatever the model has available. It can sound perfectly reasonable, and an unsuspecting user won’t have any basis to doubt it. When a business decision rests on information that nobody verified, the company is exposed, and in regulated industries the consequences extend to legal and financial liability.
Employees today aren’t willing to give up the AI tools that make their work easier and faster. So they also use publicly available ones, which feeds the growth of shadow AI on one hand and the risk of acting on wrong information on the other. In this article, we take a closer look at why AI tools produce fabricated answers. We also explain what it takes to get answers grounded in your company’s actual knowledge and how to make sure those answers can be trusted.
Why AI Makes Things Up
A large language model (LLM) learns by processing massive collections of text. During training, it picks up patterns: which words and sentences tend to appear together, what a typical answer to a given type of question looks like. When you ask it something, the response is assembled from those patterns, without reference to any specific source.
This works well enough when the question is about something widely covered in the training data. When the question is about something the model has never encountered, it still produces an answer, built on the same patterns but with no connection to the truth.
We call that a hallucination. The answer sounds plausible, fits the question, and has no grounding in fact. You might assume this is a malfunction. It isn’t. The model is doing exactly what it was designed to do, just under conditions where it has nothing real to build on.
Researchers at Stanford University measured how widespread the problem is. They put more than 200,000 legal questions to general-purpose models. On specific legal queries, the models tested at the time, including GPT-3.5, Llama 2, and PaLM 2, fabricated answers in 69% to 88% of cases. Those results reflect models available in 2023 and a single domain, so they shouldn’t be projected onto all of AI or today’s tools. They do, however, reveal a pattern that still holds: the more specialized the question and the less commonly the subject appears in text, the more likely the model is to guess.
From the AI tool’s perspective, a company’s internal knowledge is exactly that kind of subject. No contract or internal procedure was part of any model’s training data, so questions about their content always land in territory where all the model can do is guess.
How RAG Works
If you want an AI tool to answer based on your company’s actual data, you have to make that data available to it. RAG (retrieval-augmented generation) is a method for building AI tools in which the model searches a designated collection of documents before generating a response.
The user’s query first goes to a search engine. It scans the company’s knowledge base and pulls out the passages most relevant to the question. The model then receives both the question and those retrieved passages, and builds its answer from them.
The model’s job is now to summarize and interpret material that has been placed in front of it. That’s a task it handles well. The answer can also be verified, since the source passages it drew on are known.
It might seem like you could get the same result by pasting documents straight into the prompt. For a few pages, that works. But a company knowledge base typically runs to thousands of documents. The model can only process a limited amount of text at a time, and running the entire collection through it with every query would be far slower and more expensive, since model providers charge by the amount of text processed. A search engine solves both problems at once: it scans the full collection in a fraction of a second and passes the model only the passages that are relevant to the query. A well-configured system can also enforce access controls, so a marketing employee won’t see HR documents in the results.
RAG works the same way regardless of whether the model runs on the company’s own infrastructure or as an external service. We cover what to consider when choosing a solution later in this article.
The Limits of a Basic RAG Setup
How much RAG reduces AI hallucinations depends on what goes into the knowledge base and how it’s prepared. Pulling all of a company’s valuable information into one place tends to be harder than it sounds.
An organization’s knowledge is usually scattered. Procedures live on the intranet, contracts sit in a document management system, and certain decisions exist only in email threads. Some of these resources are restricted to specific employees, and others can’t leave the corporate network at all. Building a single knowledge base from all of this takes real effort.
It’s also worth knowing that the search engine may not distinguish a current procedure from a three-year-old version. The model will faithfully describe whichever passage it receives and point to the source document. The answer will have proper source attribution and still mislead the employee.
The search engine doesn’t work with whole documents, either. After being loaded into the system, documents are broken into smaller passages. A passage on its own, such as a table without a caption or a single clause from a set of regulations, may not carry enough context to be matched to the right query.
A second study by the same Stanford team shows what this looks like in commercial products. The researchers tested commercial tools built on RAG and marketed as hallucination-free. Those tools gave incorrect answers in 17% to 33% of queries. The study’s authors note that the vendors had not provided evidence backing their claims and had not precisely defined what they meant by “hallucination.”
RAG clearly reduces AI hallucinations, but it doesn’t eliminate them. Models without access to source material hallucinated several times more often in the earlier study, so the difference is substantial. The reliability of answers, even in a RAG-enabled tool, depends on where the knowledge in the base comes from, whether it’s current, and whether the right passages can be found.
Good enterprise RAG solutions address all of these issues. They keep up with document changes by detecting what has been added or modified and processing only those files. Every passage is stored with its context, so the search engine reaches for the right information.
Vector Search and Knowledge Graphs
RAG can use different search mechanisms, and they complement each other.
Vector search works by comparing meaning. Each document passage is run through a model that encodes its meaning as a string of numbers, and those encodings are stored in a vector database. The user’s query goes through the same model and is also encoded as a string of numbers. The system compares the query’s encoding against the stored passage encodings and picks the closest matches. This means an employee can ask in their own words and still get the right section of a procedure, even if it’s phrased completely differently.
A vector database works best when the answer to a question can be found in a single location. It’s less effective when the answer requires connecting information from multiple documents. An employee asks why a contract with a supplier was terminated. The search engine returns passages from that contract, because those are the closest semantic match to the question. The actual reason, however, is buried in a meeting protocol from six months earlier, a document that never mentions the contract by name. The search engine compares the query against passages, not passages against each other, so it never reaches the protocol.
Relationships between documents need to be stored separately, and that’s what GraphRAG does. An AI model reads the documents, extracts concepts and the relationships between them, and records them in a knowledge graph. A relationship might look like this: Company X entered into Agreement Y. The system traces those connections and reaches the protocol where the termination decision was made.
What concepts and relationships the system looks for in documents is defined by an ontology. It organizes knowledge about a given domain by naming the concepts that appear in it and specifying what relationships can connect them. For industries like logistics, aviation, or healthcare, established ontologies already exist, so the graph schema can be set up in advance. For document collections that haven’t been structured before, the system can analyze them and propose a schema on its own.
The table below shows how the two approaches to retrieval differ.
| Vector search | Graph-based search | |
| Knowledge base structure | Document passages and their meaning encoded as strings of numbers | Document passages and their meaning, plus concepts and relationships between them |
| Search mechanism | Finds passages closest in meaning to the query | Starts from the closest passage and follows relationships between concepts |
| Best suited for | Specific information contained in individual documents | Questions that require connecting multiple facts, documents, or analyzing an entire collection |
| Example query | What is the deadline for processing a warranty claim? | Which provisions of the master agreement were amended or superseded by annexes? |
As mentioned above, the two approaches complement each other, which is why enterprise-grade solutions typically combine them.
What to Look for in an Enterprise RAG Solution
Choosing a solution starts with understanding your own situation. You need to know which documents should go into the knowledge base, which regulations apply to your company, and who should have access to what. Only against that backdrop can you see which solutions are even on the table.
Where the model runs narrows the field. The tool reaches into contracts, procedures, and customer documentation, and with an external provider’s model the retrieved passages leave the corporate network along with the employee’s query.
A model running on the organization’s own infrastructure (on-premises) or in an isolated private cloud environment solves this problem, because no query ever leaves the network. For banks, healthcare providers, and public administration, this is often the only acceptable path.
The legal basis here is the GDPR. When a query containing personal data reaches a model running outside the European Economic Area, it constitutes a transfer of personal data to a third country, and that transfer is subject to a separate set of requirements. A model running inside the company’s own network removes this issue entirely.
Some companies don’t face these restrictions and prefer to use external models. In that case, the solution is to look for a platform where sensitive data is protected by data masking. The system detects sensitive data in the query and replaces it with fictitious substitutes before anything reaches the model, then restores the original values in the response. Solutions built for European markets can recognize local identifiers such as national ID and tax numbers.
Another important criterion is whether the AI’s answers can be verified. A RAG-enabled tool should be able to show which document and which page the information came from, and quote the passage it based its answer on. With graph-based search, it can also show the chain of relationships it followed. The employee can then open the cited source and verify the answer directly. In a regulated company, this matters just as much when an auditor asks for the basis of a specific decision.
Does deploying the tool require developers? Some platforms let business teams assemble a solution from ready-made components in a visual interface, without writing code and without knowing anything about vector databases or knowledge graphs. A team that knows its own documents and knows what it’s looking for can deploy the tool on its own, without waiting in line for IT, and adjust it as needs change.
A well-built knowledge base with good search capabilities ultimately opens the door to more than just answering questions. When an AI tool can retrieve the right clause from a contract, it can also draft a letter based on it or trigger the next step in a process. RAG then becomes the knowledge layer that AI agents rely on.
FAQ
What’s the Difference Between RAG and Fine-Tuning?
Fine-tuning means retraining an existing model on a narrower dataset, which changes its internal parameters. RAG leaves the model unchanged and simply feeds it relevant documents before it generates a response. For company knowledge, two things are decisive. Documents change constantly, and with RAG you just load the updated version of a procedure, whereas a fine-tuned model only knows what it saw during its last round of training. On top of that, a fine-tuned model can’t point to where it got its information, because the answer comes from its parameters, not from a specific document. Fine-tuning is useful when you need to adjust the model’s tone or output format.
Does RAG Completely Eliminate Hallucinations?
No, but it reduces them significantly. A model without access to sources guesses from scratch and gets it wrong far more often than a RAG-enabled model, which receives the relevant passage and builds its answer from there. Some risk remains, because an LLM generates text based on probability, so even with a well-matched passage it can occasionally draw a conclusion slightly broader than the source supports. That’s why the quality of the knowledge base and the ability to verify answers against the cited document matter just as much as the method itself.
Does RAG Work With On-Premises Data?
Yes. The entire knowledge base and model can run on the organization’s own infrastructure or in an isolated private cloud environment, which means queries and documents never leave the corporate network. For banks, healthcare providers, and public administration, this is often a prerequisite, and without it deployment simply isn’t an option. Companies that don’t face these constraints can use external models and protect sensitive data through data masking.
How Long Does RAG Take to Deploy?
Most of the time goes into preparing the data. You need to determine which documents go into the knowledge base, who should have access, and how to keep everything current. A company with well-organized documentation in a single system will get up and running faster than one whose knowledge is spread across a dozen locations in multiple versions. Deployment speed also depends on whether the platform requires developer involvement or whether a business team can handle it on their own.
Sources
1. Stanford RegLab / HAI, Hallucinating Law: Legal Mistakes with Large Language Models are Pervasive, 2024 – https://law.stanford.edu/2024/01/11/hallucinating-law-legal-mistakes-with-large-language-models-are-pervasive/
2. Stanford RegLab, Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools, 2024 – https://reglab.stanford.edu/publications/hallucination-free-assessing-the-reliability-of-leading-ai-legal-research-tools/
3. Extentum AI, Shadow AI: Why Employees Feed Company Data Into ChatGPT, 2026 – https://extentum.ai/shadow-ai-gdpr-risks-data-leaks/
4. Extentum AI, GenAI Implementation Guide, 2026 – https://extentum.ai/genai-implementation-guide/
5. Extentum AI, AI Assistant in Business: How Does It Differ from an AI Agent, and Which Should You Choose?, 2026 – https://extentum.ai/extentum-ai-ai-assistant-vs-ai-agent-2026/
6. European Commission, Rules on International Data Transfers – https://commission.europa.eu/law/law-topic/data-protection/international-dimension-data-protection/rules-international-data-transfers_en
