Retrieval-Augmented Generation (RAG) is being positioned as a solution to the inaccuracies in generative AI. It enhances the performance of models like ChatGPT by fetching data instead of relying solely on trained memory. However, experts warn that RAG can introduce its own issues, making AI less reliable. Critics, including Rasa's CTO, argue that RAG is overhyped, relying on basic programming logic, and its efficacy is limited, with some systems achieving only 25% success rates. This casts doubt on RAG's long-term utility in business applications.
"Top web [RAG] agents still only succeed 25% of the time, which is unacceptable in real software. Developers should focus on writing clear business logic."
"RAG is just a buzzword that just means adding a loop around large language models and data retrieval. The hype is overblown."
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