Large Language Models are incredibly powerful, but they have a fundamental limitation: their knowledge is frozen at the time of training. Enter RAG—Retrieval-Augmented Generation.
The Hallucination Problem
When LLMs don't have specific information, they often 'hallucinate'—generating plausible-sounding but incorrect responses. RAG solves this by grounding responses in your actual data.
How RAG Works
- Document Ingestion: Your PDFs and wikis are indexed
- Vector Embeddings: Text is converted for semantic search
- Retrieval: Relevant chunks are retrieved for each query
- Augmented Generation: The LLM generates using retrieved context
Want to see RAG in action? Book a demo to see how we can connect your AI to your business knowledge.