Retrieval-Augmented Generation (RAG) is an innovative AI approach that combines the strengths of large language models (LLMs) with external retrieval systems to deliver more accurate, factual responses. While traditional chatbots and LLMs excel at understanding and generating language, they often struggle with providing factually correct information. RAG addresses this limitation by integrating a system specifically designed to retrieve reliable facts.
Large language models are trained on vast datasets of text—trillions of words from books, websites, and other sources. While this training gives them an exceptional grasp of language, it doesn’t equip them to discern what’s factually accurate. For example:
RAG bridges this gap by enhancing the factual reliability of AI systems.
RAG combines two key components:
Here’s how RAG operates step-by-step:
RAG represents a significant advancement in making AI more trustworthy and practical. By addressing the factual limitations of LLMs, it empowers organizations to deploy AI solutions that are not only conversationally fluent but also grounded in truth.
Contact Launch to learn how Retrieval-Augmented Generation can enhance your AI systems and ensure your applications deliver accurate, reliable information.
Retrieval-Augmented Generation (RAG) is an innovative AI approach that combines the strengths of large language models (LLMs) with external retrieval systems to deliver more accurate, factual responses. While traditional chatbots and LLMs excel at understanding and generating language, they often struggle with providing factually correct information. RAG addresses this limitation by integrating a system specifically designed to retrieve reliable facts.
Large language models are trained on vast datasets of text—trillions of words from books, websites, and other sources. While this training gives them an exceptional grasp of language, it doesn’t equip them to discern what’s factually accurate. For example:
RAG bridges this gap by enhancing the factual reliability of AI systems.
RAG combines two key components:
Here’s how RAG operates step-by-step:
RAG represents a significant advancement in making AI more trustworthy and practical. By addressing the factual limitations of LLMs, it empowers organizations to deploy AI solutions that are not only conversationally fluent but also grounded in truth.
Contact Launch to learn how Retrieval-Augmented Generation can enhance your AI systems and ensure your applications deliver accurate, reliable information.