# Retrieval-Augmented Generation (RAG)
# Definition
Retrieval-Augmented Generation (RAG) is a method that combines information retrieval with generative AI models to produce more accurate and contextually relevant outputs. In RAG, a retrieval system fetches relevant documents or data from a knowledge base (structured or unstructured) based on a query or prompt. This retrieved information is then provided as context to a large language model (LLM), enabling the model to generate responses that are grounded in factual, external knowledge rather than relying solely on its training data. This approach is particularly useful for tasks like answering domain-specific questions, summarizing documents, or generating reports, as it ensures the output aligns with up-to-date and verifiable information. RAG systems are commonly used in applications like customer support, healthcare, and legal document analysis.
In Retrieval-Augmented Generation (RAG):
- Embeddings represent both the documents and the user’s query.
- Vector search retrieves the most relevant documents based on similarity.
- An LLM combines retrieved information with its pre-trained knowledge to generate a coherent and accurate response.