RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is an architecture that combines LLM s with custom knowledge bases. Instead of relying solely on the model's training knowledge, RAG first retrieves relevant documents from a database and passes them as context to the LLM . The result: more accurate answers based on current and company-specific data — without expensive model retraining.
How RAG works in practice
A RAG pipeline consists of three steps: (1) Indexing — documents are split into chunks and stored as vectors in an embedding database. (2) Retrieval — for a query, the semantically most similar document chunks are found. (3) Generation — the LLM receives the question plus retrieved documents and generates a fact-based answer. For API -based web applications, RAG enables chat-based search, document Q&A, and intelligent helpdesks.
RAG vs. fine-tuning vs. in-context learning
Fine-tuning an LLM is expensive, time-consuming, and becomes stale as data changes. RAG is more cost-effective and allows real-time updates to the knowledge base without model training. In-context learning (directly in the prompt) is limited by the model's context window length. RAG overcomes these limitations through selective retrieval. For SMEs with internal documents, FAQs, or product databases, RAG is the most practical AI integration option.
RAG in web projects and AI visibility
For web applications with search functionality or internal knowledge bases, RAG is the recommended architecture. Particularly relevant: MCP -based integrations can expose RAG pipelines as tools for AI Agent s. RAG is also indirectly relevant for SEO — well-structured, citable content (semantic HTML, Schema Markup ) is more effectively found and used by search engines' RAG-based systems.