Context-aware chatbots with data engineering for multi-turn conversations

The traditional method of deploying chatbots, which only answered simple questions, has developed to the present form, where chatbots are complex conversational models able to handle a sequence of turns within a conversation. This research analyzes context-aware chatbots on moderates in data engineering approaches and state-of-the-art machine learning methods. Using such tactics, this study focuses on critical aspects like data preprocessing and feature engineering and on creating training pipelines for which this study intends to address core concerns entrenched in the challenge of achieving conversational context switching across multiple exchanges.
Keeping the context relevant is one activity that defines the multitudinous turn-taking communication processes. The research also pays special attention to the preprocessing step, which removes the noise from the data and improves the training dataset improves the training dataset. Feature engineering stands central to extracting linguistic and contextual features as a precondition for models to understand the user input and continue conversation selectively. These processes are best trained from pipelines designed for such flows, including reiterative feedback loops to help the model learn how to store and manipulate context as it adapts.
It also examines the adoption of front-end technologies to enrich the customer experience and create great customer feedback. The UI is built not only to represent the bot’s ability but also with an inglorious role of adapting to suit the user to maintain an interactive and realistic user-friendly dialog. Through the use of users’ friendly features, these interfaces act as a middle link between the complicated back-end systems and the consumers, making them more comfortable to use.