What is RAG (Retrieval-Augmented Generation)?
RAG (Retrieval-Augmented Generation) — A technique that improves AI accuracy by fetching relevant data from a vector database before generating an answer.
RAG solves one of the biggest problems with LLMs: they can only answer based on what they were trained on. By retrieving relevant documents from your own databases before generating a response, RAG ensures answers are grounded in your actual company data rather than the model’s general training.
Frequently Asked Questions
Why use RAG instead of fine-tuning?
Fine-tuning bakes knowledge permanently into the model and requires retraining when data changes. RAG pulls from a live database, so your AI always has access to the most current information without retraining.
What databases work with RAG?
RAG typically uses vector databases like Pinecone, Weaviate, or pgvector. Your documents are converted into embeddings and stored for fast semantic retrieval.
Does RAG eliminate hallucinations?
RAG significantly reduces hallucinations by grounding responses in retrieved documents, but it does not eliminate them entirely. Proper chunk sizing and retrieval tuning are critical for accuracy.