In this episode, Ian and Harry discuss RAG systems (retrieval augmented generation systems), which are critical components of AI. RAG systems allow AI models to generate responses that are augmented by retrieving information from a knowledge base. These systems are useful in scenarios where general-purpose AI models like GPT lack specific domain knowledge. RAG systems can be applied to co-pilots, chatbots, and search engines, enhancing their ability to provide relevant and accurate information. They enable the creation of domain-specific models and can be used to automate tasks and improve productivity. RAG systems optimize the output of language models by informing their responses.
Keywords
RAG systems, retrieval augmented generation systems, AI, knowledge base, domain-specific models, co-pilots, chatbots, search engines, automation, productivity
Takeaways
RAG systems are critical components of AI, allowing models to generate responses augmented by retrieving information from a knowledge base.
These systems are useful in scenarios where general-purpose AI models lack specific domain knowledge.
RAG systems can be applied to co-pilots, chatbots, and search engines, enhancing their ability to provide relevant and accurate information.
They enable the creation of domain-specific models and can be used to automate tasks and improve productivity.
RAG systems optimize the output of language models by informing their responses.