RAG enhances LLMs by enabling external knowledge retrieval

Retrieval-Augmented Generation (RAG) is a technique designed to enhance Large Language Models (LLMs) by enabling them to access and incorporate external information before generating a response. This approach addresses the limitations of LLMs, such as knowledge cutoffs and the tendency to hallucinate, by providing them with a 'superpower' to consult a vast knowledge base. A typical RAG system requires a powerful LLM, a knowledge base, an embedding model to convert text into numerical vectors, and a vector database for efficient searching, all orchestrated by a retrieval mechanism. AI

IMPACT Enhances LLM accuracy and factual grounding by integrating external knowledge, reducing hallucinations and improving relevance.

RANK_REASON The item describes a technical architecture (RAG) for improving LLMs, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• Anthropic
• Bard Ai
• GPT-3
• llama
• Llama 2
• OpenAI
• retrieval-augmented generation

• product
• infra

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