AI-powered chatbots use machine learning models trained on large amounts of conversational data to understand natural language with flexibility, allowing them to interpret varied phrasings and maintain context across multi-turn conversations. Rule-based chatbots instead follow predefined decision trees, matching customer input against specific keywords or exact phrases and offering only the responses explicitly programmed for each recognized pattern. AI-powered chatbots generally handle ambiguity and unexpected phrasing far better, reducing the frustrating dead-ends common with rule-based systems when a customer's wording falls outside anticipated patterns. AI-powered systems also typically improve over time as they process more interactions, while rule-based systems require manual updates to expand their capability. The tradeoff is that AI-powered chatbots require more sophisticated infrastructure and ongoing model management, while rule-based systems are simpler to build and more predictable for narrowly scoped use cases.
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