AI concepts
RAG (Retrieval-Augmented Generation)
An architecture in which, before generating an answer, an LLM receives dynamically retrieved fragments from the client's knowledge base instead of relying solely on its training knowledge. RAG lowers the hallucination rate from 10 to 27 percent down to 0.5 to 3 percent for information tasks, and makes AI deployment possible without fine-tuning the model.
Primary source: Meta AI Research 2020 (original paper), AWS Bedrock Knowledge Bases 2025
RAG was described academically by Patrick Lewis and the Meta AI team in the paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" from May 2020. Mass commercialisation followed in 2023 to 2024 as a counter to two limitations: the model does not know the client's data, and the model hallucinates on specialist topics.
The technical mechanism
The architecture has four components. First, a vector database (Pinecone, Weaviate, Qdrant, ChromaDB) holding embeddings of the client's knowledge base content. Second, a retrieval engine that, for each query, finds the k most similar fragments (top-k is usually 5 to 20). Third, a prompt assembler that combines the user's query with the selected fragments. Fourth, the LLM that generates the final answer from the enriched prompt.
The effect: the model answers based on specific documents rather than general knowledge. It can cite sources. Hallucinations drop.
What RAG solves, and what it does not
It solves: answering questions about company documents, handling changing knowledge (updated by adding to the base rather than retraining the model), and traceability (every answer has a source).
It does not solve: tasks that require reasoning beyond the supplied documents, generating new knowledge, or problems with the quality of the knowledge base (garbage in equals garbage out).
When to use it and when not to
RAG makes sense when the knowledge base changes more than once a month, source citations are required, or fine-tuning would be cost-irrational.
RAG does not make sense when the knowledge base is small (fewer than 100 documents), specialist domain reasoning is required (medicine, law), or latency is critical (RAG adds 200 to 800 ms per query).
The scale of enterprise deployment
AWS Bedrock Knowledge Bases and Azure AI Search report RAG as the most frequently chosen deployment pattern in 2025, with a share of over 60 percent of enterprise GenAI projects. In Polish companies in 2025 to 2026, RAG dominates in three use cases: searching the internal knowledge base, a customer support chatbot with access to documentation, and generating commercial proposals from templates.
The cost of deployment
An average RAG for a knowledge base of 10,000 documents, with monitoring and security: 60 to 200 thousand zloty one-off, and 3 to 8 thousand zloty per month for maintenance (vector database hosting, the LLM API, monitoring). The cost can be optimised with an open-source vector database (self-hosted Qdrant) and local LLMs (Llama, Mistral).
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