This December, I'm showing how naive chatbots fail when customers ask for details and how AI agents step up.
Previously, we saw how naive chatbots fail at clarifying questions and context shifts.
Today, we show how they struggle with requests involving numbers. A customer might ask for shoes with a heel height over a certain number. A naive chatbot will ignore the number and just show all heels, while an AI agent will apply the correct filter.
This December, I'm highlighting the limitations of simple AI chatbots in online retail and demonstrating how AI agents enhance customer interactions.
In yesterday's issue, we saw how naive chatbots fail immediately due to lack of contextual reasoning. Today, we highlight a slightly different but equally challenging scenario: dealing with context shifts during the conversation.
A customer often changes their mind mid-conversation. A naive Retrieval Augmented Generation (RAG) chatbot may struggle to incorporate these shifts, while an AI agent will dynamically adapt, refining its recommendations based on updated customer input.
This December, I'm highlighting the limitations of simple AI chatbots in online retail and demonstrating how AI agents enhance customer interactions.
Each day, we'll explore a common challenge faced by naive Retrieval Augmented Generation (RAG) chatbot systems and show how AI agents overcome them.
Todays topic is about how AI agents improve naive chatbots by asking clarifying questions.
Naive RAG (retrieval augmented generation) is becoming obsolete - AI agents are taking over.
We'll talk about the 4 main limitations of naive RAG and why agents are taking over.
Chatbots are becoming increasingly popular for providing quick and efficient customer support, answering questions, and helping users navigate through complex tasks.
In this blog post, I'll walk you through the process of building an AI-powered chatbot using ReactJS, OpenAI API and AWS Lambda.