RAG (Retrieval-Augmented Generation)
An AI technique that combines a language model with an external knowledge source, so that answers are based on current, company-specific information.
What is RAG?
RAG (Retrieval-Augmented Generation) is an architectural pattern where an AI language model is enriched with information from external sources before generating an answer. Instead of relying solely on the model's training data, a RAG system first searches a knowledge base, such as documents, manuals, or databases. The found information is provided as context to the language model, resulting in answers that are current, relevant, and verifiable.
How does RAG work?
A RAG system works in three steps. First, documents and other sources are processed into embeddings and stored in a vector database. When a question comes in, the most relevant fragments are retrieved via staged retrieval based on semantic similarity. These fragments are then provided as context to the language model, which generates a substantiated answer based on them. At Wabber, this entire process runs on a proprietary 128GB VRAM cluster in the Netherlands.
Example
A logistics company with 500 pages of process manuals implements a RAG chatbot through Wabber. Employees ask questions about procedures, and the chatbot automatically searches all manuals to provide the correct answer, including source references. Where employees previously spent minutes searching through thick binders or on the intranet, they now receive an accurate answer within seconds that directly references the relevant document.
Why is RAG important?
RAG is the key to reliable AI for businesses. Without RAG, a language model may provide outdated or incorrect information, also known as hallucination. With RAG, every answer is supported by your own documentation and data, drastically increasing reliability. Wabber implements RAG solutions as part of chatbot and chat widget products, ensuring employees and customers always have access to accurate, up-to-date knowledge.
Related solutions
Frequently asked questions
What is the difference between RAG and a regular chatbot?
A regular chatbot answers solely based on its training data, which may be outdated. A RAG chatbot first searches your own knowledge base with every question and bases the answer on current, company-specific documents. This makes RAG answers more reliable and verifiable.
What documents can a RAG system process?
A RAG system can process virtually all textual sources, including PDFs, Word documents, web pages, manuals, and databases. Wabber's RAG pipeline includes scrapers that automatically process websites and documents, generate embeddings, and store them in a vector database.
Where does Wabber's RAG pipeline run?
Wabber's complete RAG pipeline runs on a proprietary AI cluster with 128GB VRAM in the Netherlands. This guarantees that your data does not leave the country and complies with strict privacy requirements. The cluster can also be placed on-premise at the client's location for maximum control.
How does RAG prevent an AI from giving incorrect answers?
RAG prevents hallucination by providing the language model with factual context from your own documentation before it generates an answer. The model bases itself on this retrieved information instead of guessing. Additionally, the system can display source references, allowing users to verify the answer.
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