Artificial Intelligence and Retrieval Augmented Generation (RAG)
Artificial Intelligence and Machine Learning have been revolutionized by Large Language Models (LLMs) in recent times. These models have gained considerable attention for their ability in Natural Language Processing, generation, and understanding, with ChatGPT as a prime example. Despite their impressive capabilities, LLMs still have drawbacks, such as inaccuracy and outdated outputs.
Retrieval Augmented Generation (RAG) is an AI-based framework that addresses these limitations by providing LLMs with access to accurate and up-to-date information from external knowledge bases. RAG ensures precision and transparency in the output generated by LLMs, offering more reliable and context-aware communication.
The advantages of RAG include improved response quality, access to current information, and transparency, reducing the possibility of information loss and hallucination and lowering computational costs. RAG integrates retrieval-based techniques and generative models, enabling LLMs to provide accurate and contextually rich responses.
In conclusion, RAG is a potent technique that holds great potential for improving the accuracy and reliability of LLM-powered applications. With RAG, LLMs can be more informed and productive, leading to improved AI applications with increased confidence and accuracy.