At the present time, ChatGPT possesses a sufficiently extensive information base on historical styles, which, with proper "navigation", can be used by architects. If, for example, you ask II to give a definition of the baroque style, then first he will give the general information spread on the Internet, which consists of arguments about the external manifestations of the style, expressed in the decorative design of facades and interiors. But when introducing a clarifying remark, for example, "illusion", a more in-depth and detailed discussion of the topic is provided with the selection of signs, characteristic features and methods inherent in this style. If we start talking about the architectural trends of the 20th century, then the complexities will only be added. For example, despite having information about expressionism, its key features, the works of architects, bright representatives of this direction, II still does not have a sufficient quantitative base of images and clues that characterize this direction. The lack of information is reflected in the resulting models

Building interactive chatbots with high responding accuracy involves a combination of methodologies and technologies. Here's a breakdown of some effective approaches:

1. Natural Language Processing (NLP): Utilize NLP techniques to understand and interpret user input. NLP allows chatbots to comprehend the nuances of human language, including intents, entities, and context. Popular NLP frameworks include:

- Natural Language Toolkit (NLTK)

- spaCy

- Google Cloud Natural Language API

- Microsoft Azure Text Analytics

2. Machine Learning (ML): Implement machine learning algorithms to improve the chatbot's understanding and responses over time. ML models can be trained on large datasets of conversational data to enhance accuracy. Common ML techniques used in chatbots include:

- Supervised learning for intent classification and entity recognition.

- Reinforcement learning for dialogue management.

- Transfer learning to leverage pre-trained language models like BERT, GPT, or their variants.

3. Dialog Management: Develop a robust dialog management system to handle multi-turn conversations effectively. Dialog management involves keeping track of context, managing conversation flow, and generating appropriate responses. Tools and frameworks for dialog management include:

- Rasa

- Microsoft Bot Framework

- IBM Watson Assistant

- Dialogflow by Google

4. Entity Recognition: Implement entity recognition to extract relevant information from user input. Entities can represent specific objects, dates, locations, or any other relevant data. Accurate entity recognition enhances the chatbot's ability to understand user requests. Techniques for entity recognition include rule-based approaches, statistical models, and deep learning.

5. Intent Classification: Classify user intents to determine the user's goal or purpose. Intent classification helps the chatbot identify the user's underlying request and respond appropriately. This can be achieved using machine learning classifiers trained on labeled intent data.

6. Knowledge Base Integration: Integrate a knowledge base or database to provide accurate and relevant responses to user queries. Knowledge bases can be populated with structured information or unstructured content like FAQs, manuals, or articles. Retrieval-based or generative models can be used to access and deliver information from the knowledge base.

7. Continuous Evaluation and Improvement: Regularly evaluate the chatbot's performance using metrics like accuracy, engagement, and user satisfaction. Collect user feedback to identify areas for improvement and iterate on the chatbot's design, NLP models, and dialog flows accordingly.

By combining these methodologies and technologies, developers can create interactive chatbots with high responding accuracy that effectively meet user needs and expectations.

The difficulty in learning AI arises in the determination of styles, trends, and conceptual directions. In architectural theory there is no unified conceptual apparatus and a developed system of definitions of accepted terminology. This problem is especially acute when studying the modern history of architecture of the 20th and 21st centuries. The confusion of terminology makes it difficult to teach students, and not only artificial intelligence. There is a need to use the principles of taxonomy when creating a classification and systematization of architectural trends. Introduction to uniform concepts, accurate and comprehensive formula determinations. One and the same conceptual directions of modernism and postmodernism were called differently. To eliminate confusion, facilitate the assimilation of the material and provide students with the opportunity to freely orient themselves in the space of modern and new architecture, the scientific community has repeatedly turned to the solution of this problem. More than ten years ago together with Professor D. D. Omuraliev we also addressed the questions of the systematization of architectural trends and trends. On the basis of the analysis of works on the history of architecture, the structure of the grouping of currents of the XX and XXI centuries was proposed. within large architectural styles (early and late modernism, postmodernism and panmodernism). Parallels were drawn between different tracts, correspondences were established, and brief (and thus easily remembered by students) definitions of each architectural trend were given, which were accompanied by illustrations for clarity. This same principle can be used for the training of artificial intelligence with the involvement of a more detailed illustrative series.