The earliest chatbots were essentially interactive FAQ programs, which relied on a limited set of common questions with pre-written answers. Unable to interpret natural language, these FAQs typically required users to select from simple keywords and phrases to advance the conversation. Such rudimentary, traditional chatbots are unable to process complex questions, nor answer simple questions that haven’t been predicted by developers.
Over time, chatbot algorithms became capable of more complex rules-based programming and even natural language processing, enabling customer queries to be expressed in a conversational way. This gave rise to a new type of chatbot, contextually aware and armed with machine learning to continuously optimize its ability to correctly process and predict queries through exposure to more and more human language.
Modern AI chat now uses natural language understanding (NLU) to discern the meaning of open-ended user input, overcoming anything from typos to translation issues. Advanced AI tools then map that meaning to the specific “intent” the user wants the chatbot to act upon and use conversational AI to formulate an appropriate response. These AI technologies leverage both machine learning and deep learning, different elements of AI, with some nuanced differences, to develop an increasingly granular knowledge base of questions and responses informed by user interactions. This sophistication, drawing upon recent advancements in large language models (LLMs), has led to increased customer satisfaction and more versatile chatbot applications.
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