Chatbots have evolved from simple keyword matching to sophisticated language understanding. Modern chatbots leverage LLMs for natural conversation.
Rule-based chatbots use pattern matching and decision trees. Simple to build but rigid. Good for FAQ responses and structured flows.
ML chatbots use intent classification and entity extraction. Train on labeled data to recognize user intent. Tools: Rasa, Dialogflow, Botpress.
LLM-powered chatbots (GPT, Claude, Gemini) understand context and nuance. They generate human-like responses without explicit training data. Fine-tuning customizes behavior.
Architecture: message intake, intent recognition, context management, response generation, and delivery. Add memory for multi-turn conversations.
Best practices: define clear personality, handle edge cases gracefully, provide human handoff, log conversations for improvement, and test with diverse inputs.