What is RAG?
Retrieval-Augmented Generation (RAG) is a technique that connects an AI language model to an external knowledge source, such as your company's documents, so the AI can answer questions based on that specific information.
What is RAG?
RAG stands for Retrieval-Augmented Generation.
Breaking it down:
- Retrieval: searching for relevant information from your documents
- Augmented: adding that information to the AI's context
- Generation: the AI generates an answer using the retrieved information
In simple terms: instead of relying only on what the AI learned during training, RAG allows the AI to also look up relevant information from your specific documents before generating an answer.
This solves a fundamental problem: AI models have a training cutoff date and do not know about your company's internal data, your product documentation, or recent events.
Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence
A Simple Analogy
Think of RAG like an open-book exam:
Without RAG: the AI answers from memory alone (what it learned during training). It might not know company-specific policies or recent information.
With RAG: before answering, the AI is allowed to search through your documents and find the relevant pages. It then uses those pages to formulate an accurate, specific answer.
The AI is still the one generating the answer. But RAG gives it a chance to look up relevant material first.