Conversational AI vs generative AI: Main functions compared

Conversational AI and generative AI serve different but increasingly complementary purposes: conversational AI is designed for context-aware text or voice interactions, while generative AI creates original text, code, images, video, and audio from prompts. Conversational systems combine natural language understanding, dialogue management, natural language generation, company knowledge sources, and sometimes large language models to assist customers, complete workflows, and escalate issues when necessary. Generative systems typically use large language models, transformer architectures, and diffusion models trained on broad datasets to produce content, though they can introduce hallucinations or reflect biases in their training data. Both are generally considered weak, limited-memory AI, but conversational AI focuses on resolving requests and maintaining dialogue, whereas generative AI focuses on content creation. Businesses may choose conversational AI for customer support, appointment scheduling, lead qualification, and digital assistants, or generative AI for marketing, design, writing, coding, and summarization, while combining both to create experiences such as a support interaction followed by a personalized generated proposal or email.

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