“Architects may become a thing of the past” says ChatGPT
In summary, incorporating a knowledge base into an AI-based chatbot system brings numerous benefits. It provides access to comprehensive information, improves response accuracy, and ensures consistency in responses. This allows chatbots to tailor responses to individual users, providing a more engaging and personalised conversational experience. As the knowledge base grows, chatbots can access and retrieve information faster, enabling them to handle higher volumes of user inquiries without sacrificing response time or accuracy.
Dialog management plays a vital role in the operational mechanics of AI-based chatbots. It involves managing conversation context, recognizing user intents, extracting entities, maintaining dialog state, generating contextually relevant responses, and handling errors. By leveraging ai chatbot architecture NLP techniques, chatbots can effectively understand user inputs, generate meaningful responses, and deliver engaging and natural conversations. The processing of human language by NLP engines frequently relies on libraries and frameworks that offer pre-built tools and algorithms.
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An entity is a tool for extracting parameter values from natural language inputs. For example, the system entity @sys.date corresponds to standard date references like 10 August 2019 or the 10th of August [28]. Domain entity extraction usually referred to as a slot-filling problem, is formulated as a sequential tagging problem where parts of a sentence are extracted and tagged with domain entities [32].
The chatbot stems from a long-term business vision to transform the customer relationship, optimize management costs, and offer ever more helpful and user-friendly experiences. With advancements in AI technologies such as natural language processing (NLP) and machine learning (ML), chatbots have become increasingly sophisticated and capable of understanding context, sentiment, and intent. The chatbot architecture I described here can be customized for any industry.
Natural Language Processing (NLP)
A sentence (stimuli) is entered, and output (response) is created consistent with the user input [11]. Eliza and ALICE were the first chatbots developed using pattern recognition algorithms. The disadvantage of this approach is that the responses are entirely predictable, repetitive, and lack the human touch. Also, there is no storage of past responses, which can lead to looping conversations [28]. The reduction in customer service costs and the ability to handle many users at a time are some of the reasons why chatbots have become so popular in business groups [20].
In this section, we will delve into the significance of NLP in the architectural components of AI-based chatbots and explore its operational mechanics. These chatbots have the ability to learn and improve over time through data analysis and user interactions. If the bot still fails to find the appropriate response, the final layer searches for the response in a large set of documents or webpages. It can find and return a section that contains the answer to the user query.
