RAG (Retrieval-Augmented Generation)
Anchoring AI in your data for reliable answers.
RAG (Retrieval-Augmented Generation) is an architecture that combines information retrieval with text generation by an LLM. Before generating a response, the system searches for relevant documents in a knowledge base and uses them as context. This reduces hallucinations and ensures responses anchored in real, up-to-date data.
Iradia uses a RAG architecture to generate content anchored in your real monitoring sources. Every piece of content produced is traceable and verifiable, eliminating hallucinations.
Why is RAG better than an LLM alone?
An LLM alone can fabricate information. RAG provides it with verified sources as context, which anchors its responses in reality and makes them more reliable and current.
Does RAG work in real time?
Yes. Unlike fine-tuning which freezes knowledge, RAG queries an updated database with each request. Iradia continuously updates its sources through monitoring.
What types of data can be used with RAG?
Any text type: articles, reports, documentation, emails, transcriptions. Iradia integrates your RSS feeds, news, and trends as the knowledge base for RAG.