This December, I'm showing how naive chatbots fail and how AI agents make shopping easier.

We've seen naive chatbots fail at clarifying questions, context shifts, numerical requirements, multiple requests in one query, price filters and style suggestions.

Today, the customer wants shoes in burgundy or maroon. The naive chatbot finds nothing and just says no. The AI agent, however, asks for details, then shows similar colors that might appeal to the customer.

This December, I'm showing how naive chatbots fail and how AI agents make shopping easier.

We've seen naive chatbots fail at clarifying questions, context shifts, numerical requirements, multiple requests in one query and price filters.

Today, the customer wants shoes that match a blue dress. The naive chatbot just returns random blue shoes. The AI agent asks a clarifying question, then finds shoes that fit the style request.

This December, I'm showing how naive chatbots fail at simple requests that matter to online shoppers, while AI agents rise to the challenge.

We've seen naive chatbots fail at clarifying questions, context shifts, numerical requirements and multiple requests in one query.

Today, the customer asks for a price filter: "I need women's heels under $75." The naive chatbot ignores the price filter and fails, but the AI agent applies the numeric filter to find the right match.

This December, I'm continuing to show how naive chatbots struggle with complex shopping requests.

We've seen them fail at clarifying questions, context shifts, and numerical requirements.

Today, let's see how they fail when a customer asks for more than one product in a single message. A naive chatbot will often treat the entire query as one block of text and return irrelevant results. An AI agent, however, can break the request down and return results for each item accurately.

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.

Are you looking to build a chatbot that can recommend products to your customers based on their unique profiles? Here's a step-by-step guide that shows you how to build a chatbot using embeddings to match a user's profile with relevant products from a company's database.