RAG (Retrieval-Augmented Generation).

RAG (Retrieval-Augmented Generation) is an architecture for AI systems that combines two components: a retrieval system (searches for relevant information from external sources) and a generative language model (formulates an answer based on the found information). Instead of relying solely on its training knowledge, a RAG system can search current documents, databases, or websites and incorporate the results into its answer.

Why is RAG important? LLMs like GPT-4 or Gemini have a knowledge cutoff — they only know information up to a certain date and can 'hallucinate' (generate plausible-sounding but incorrect answers). RAG solves both problems: The retrieval system finds current, factually correct information, and the LLM formulates a comprehensible answer with source attribution. Perplexity AI and Google AI Overviews use RAG architectures.

For businesses, RAG enables custom AI assistants: A chatbot that searches your own knowledge base, product catalogs, and support documentation to answer customer inquiries specifically — instead of giving generic LLM answers. DLM Digital implements RAG-based systems for clients who need AI assistants with company-specific knowledge — from conception through technical implementation to ongoing operations.