Retrieval-Augmented Generation (RAG) Enhances Large Language Model Accuracy and Reduces Hallucinations
Retrieval-Augmented Generation (RAG) is an AI architecture designed to improve the accuracy and relevance of responses from Large Language Models (LLMs) by connecting them to external knowledge bases. LLMs, while powerful, often 'hallucinate'—generating false or misleading information with confidence—due to limitations in their training data or context. RAG addresses this by allowing LLMs to access and integrate real-time, external information. The process involves several steps: an external knowledge base is created from various sources (websites, PDFs, documents), data is chunked and vectorized into numerical representations, a retriever model searches this database for relevant information based on user queries, and an integration layer combines the original query with the retrieved information to create an augmented prompt. Finally, a generator produces the response. This framework helps overcome issues like limited context and outdated information that can lead to inaccurate outputs from standalone LL...
