Why does Chatref use retrieval-augmented generation (RAG)?
Retrieval-augmented generation (RAG) is used to ensure that answers come from known, verified content instead of general knowledge or assumptions. Chatref uses RAG so that responses are accurate, grounded in business data, and limited to the information provided by the company.
Why general AI answers are unreliable for business use
General AI systems are trained on large amounts of public data. While they can generate fluent responses, they may produce answers that are outdated, incomplete, or incorrect. These systems often attempt to fill gaps when information is missing, which can lead to hallucinations.
For business use cases such as customer support or documentation, guessing or inventing answers can create confusion and reduce trust.
What retrieval-augmented generation (RAG) does differently
RAG changes how answers are produced. Instead of generating a response first, the system retrieves relevant information from a defined set of content and then generates an answer using only that information.
This means the AI does not rely on general knowledge or external sources. It responds only within the boundaries of the connected content, which is a key difference from open-ended chat systems often discussed on the comparison page.
How retrieval-augmented generation works
Step 1: Business content is stored
Business content such as website pages, documentation, and FAQs is stored in a structured way so it can be searched when a question is asked. This content becomes the single source of truth for answers.
This process is part of how Chatref works.
Step 2: Relevant information is retrieved
When a question is asked, the system searches the stored content to find the most relevant sections. Only information related to the question is selected, while unrelated content is ignored.
This retrieval step ensures the system focuses on accuracy rather than coverage.
Step 3: Answers are generated from retrieved data only
After retrieval, the system generates an answer using only the selected information. If the required information is not found, the system does not guess or create new details.
This approach is also used when answering customer questions automatically or when making documentation searchable with AI.
How RAG improves accuracy and trust
By retrieving information before generating an answer, RAG ensures that responses are grounded in actual content. This makes answers more predictable, consistent, and easier to trust.
Users receive the same answer for the same question as long as the underlying content remains unchanged.
When RAG is the right approach
RAG is most effective when:
- Information is documented
- Accuracy is more important than creativity
- Answers must stay within defined boundaries
- Businesses need control over what the AI can say
It is not designed for open-ended conversations or creative tasks.
What happens when information is missing?
If the connected content does not contain the information needed to answer a question, the system responds by indicating that the answer is not available. It does not attempt to infer or generate speculative responses.
This behavior is further explained in the FAQ.
Summary
Chatref uses retrieval-augmented generation to ensure that answers are generated only from business-provided content. By retrieving relevant information before generating responses, RAG improves accuracy, reduces hallucinations, and provides reliable answers for business use cases.