Glossary · RAG · Enterprise AI
What is RAG in artificial intelligence?
The technique that makes LLMs answer with your own data.
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The technique that makes LLMs answer with your own data.
+300
Projects delivered
15+
Years of experience
100%
Senior team
RAG stands for Retrieval-Augmented Generation: the technique that allows a language model (LLM) like GPT-4 or Claude to answer questions using your company's specific information, rather than being limited to its training knowledge. Instead of fine-tuning the model — expensive, slow and static — RAG retrieves relevant documents in real time and adds them to the question's context. It is the technical foundation of enterprise AI systems that actually work in production. See our AI integration service.
In practice, RAG allows building chatbots that answer about your internal documentation, agents that query your knowledge base, semantic search systems over contracts or case files, and assistants that know the current state of your business. Dribba implements RAG systems in production for companies that need their AI to respond with proprietary data — not generic information. See AI agent use cases.
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Frequently asked questions
No. Fine-tuning modifies the model's weights with new data — it is more expensive, requires labelled data and the knowledge becomes 'frozen' in time. RAG retrieves information in real time from an updateable knowledge base — it is more flexible, cheaper to maintain, and generally more suitable for business knowledge that changes. In most enterprise cases, RAG outperforms fine-tuning in ROI.
You need four components: documents or data to index (PDFs, databases, wikis), a vector database (Pinecone, Weaviate, pgvector), an embeddings model to convert text to vectors, and an LLM to generate responses (GPT-4, Claude, Gemini). Dribba implements the complete pipeline, including ingestion, indexing, retrieval and the final user interface.
A basic RAG system (chatbot over internal documentation) starts at €15,000–25,000 in development. Monthly operating costs — embeddings APIs + LLM + vector database — are typically between €200 and €2,000/month depending on query volume. For more complex projects with multiple data sources and agentic flows, development can reach €60,000+.
The most common are: internal documentation chatbot (manuals, procedures, FAQs), semantic search over contracts or legal files, customer support assistant powered by the product knowledge base, and agents that query CRM or ERP data in real time to answer business questions. In all these cases, RAG is more efficient than fine-tuning.
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