We used Rasa’s features in our Python code to get the best results. Rasa’s natural language processing (NLP) helps the chatbot understand what users want (intents) and find key details in their messages (entities). By adding these Rasa tools to our Python setup, we made sure the bot can correctly understand and respond to users based on their needs.

We also trained the chatbot using real conversations so it could learn different ways people ask the same thing. This made the bot more accurate and helpful. To improve performance, we fine-tuned the model and tested it with different types of messages. As a result, the chatbot can now provide clear, quick, and relevant answers, making the user experience smooth and efficient.

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