NLP Techniques for creating AI Chatbots

Learn how rule-based keyword responses evolved into encoder-decoder and transformer models, enabling conversational chatbots to generate cohesive replies using thought vectors and attention mechanisms.

Build the base client and server using a WebSocket connection, with a step-driven actions flow, intents and entity predictions, and a data and library structure.

Implement a small flask-based interpreter server, connect it with actions, and manage multiple interpreters to improve intent accuracy, log results, and prepare for handling detected entities and menu matching.

Encode words and sentences into embeddings to measure similarity with a pre-trained model, then use them to filter items and generate training data for entity recognition.

Explore building a menu or help flow by adding a new interpreter, mapping intents to help and menu actions, and testing navigation to menu, help, and confirm order.

Extend the architecture by enabling listing and selecting items, confirming orders, filtering results, and handling cancellation through a dedicated list options interpreter and helpers.

Finalize the chatbot flow by handling yes, no, and specific responses to confirm, list options, or finish. Introduce the final order interpreter and outline future data generation and verifications.

Gather and analyze user interactions to build training data for chatbots, using disambiguation to clarify intents and logging conversations for iterative data improvement.