RAG (Retrieval Augmented Generation)
RAG (retrieval augmented generation) is a technique in which a language model, before answering a question, retrieves relevant information from an external knowledge source, often a vector database, and inserts it into the context. On that basis the model produces its answer. Hallucinations are thus reduced and current, company-specific knowledge becomes usable without retraining the model.
Also known as: RAG, retrieval-augmented generation
What is RAG (retrieval-augmented generation)?
RAG stands for retrieval-augmented generation. The technique combines a large language model with an external knowledge source. Before the model words an answer, the system searches specifically for suitable information and passes it to the model as additional context.
The advantage is obvious: the language model does not have to have everything stored in its training data but can reach current, specific or confidential information. Answers can thus be produced from internal documents, manuals or databases the model never knew at all.
How does RAG work, step by step?
First the knowledge documents are split into smaller sections, a process known as Chunking is called. Each section is then turned into a vector, a so-called Embedding, which represents its meaning numerically. These vectors are stored in a Vector database stored.
When a user asks a question, it too is turned into an embedding. Through a vector search the system finds the most similar sections. In the augmentation phase these are added to the original prompt. In the final step, generation, the language model produces the answer on the basis of the question and the retrieved information.
What are the benefits of RAG?
The most important advantage is reducing hallucinations, that is invented or false statements. Because the model bases its answer on specifically retrieved evidence, the risk of it generating plausible-sounding but untrue information falls. The sources used can often also be cited directly.
RAG also allows current, company-owned knowledge to be used without expensively retraining the language model. New documents are simply taken into the Knowledge base taken in and available at once. That makes RAG a cost-efficient, flexible way to adapt language models to specific use cases.
Where is RAG used?
RAG underpins many in-house AI applications. Typical examples are knowledge assistants that answer staff questions about internal policies or products, and customer service chatbots able to give information from current manuals and FAQ databases.
RAG is also used in research, document analysis and evaluating large bodies of knowledge. Wherever reliable, evidenced answers from a defined body of knowledge are wanted, the technique plays to its strengths and makes an organisation's knowledge usable for AI applications.
What makes a good RAG solution?
A RAG application's quality depends heavily on how the knowledge base is prepared. A well-considered chunking strategy, high-quality embeddings and a precise vector search decide whether the relevant information is actually found and passed to the model. Data upkeep plays a central part too.
Added to that are aspects such as data protection, access rights and how current the sources are. Elisabit supports companies in designing and building RAG systems that give reliable answers and open up existing knowledge safely and efficiently.
Frequently asked questions
What does the abbreviation RAG stand for?
RAG stands for retrieval-augmented generation. Before answering, a language model retrieves relevant information from an external knowledge source. This is inserted into the context and worked into the answer.
How does RAG help against hallucinations?
Because the model bases its answer on specifically retrieved evidence, it has to reconstruct or invent information from its training knowledge less often. That reduces the risk of false statements considerably. The sources used can often also be cited directly.
What is a vector database in the context of RAG?
A vector database stores the knowledge sections' embeddings, that is numerical representations of their meaning. On a query it finds the best-matching sections through a similarity search. These are then passed to the language model as context.
Does RAG require retraining the language model?
No, that is precisely an advantage of RAG. New knowledge is simply added to the external knowledge base and is available at once. No expensive retraining of the model is needed.
Related terms
An LLM is an AI language model that understands and produces text by predicting the most likely next word.
Generative AI independently creates new content — text, images, audio or code — based on patterns it has learned.
MCP is an open standard that defines how AI applications connect to external tools and data sources.
An AI system that pursues goals on its own: perceiving, planning, using tools and acting across several steps.
Context engineering shapes the information an LLM receives so that its answers become more precise and more reliable.
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