AI assistants are software systems designed to perform tasks or provide information through natural language interaction. They leverage natural language processing (NLP), speech recognition, and machine learning to understand user input and generate relevant responses or actions. Common use cases include scheduling, search, summarization, task automation, and answering domain-specific queries.

Examples range from general-purpose systems like Siri, Alexa, and Google Assistant to specialized tools in customer support, coding (e.g., GitHub Copilot), or productivity (e.g., Notion AI). Many are built on large language models (LLMs), which allow for more flexible, context-aware dialogue.

AI assistants can be rule-based, retrieval-based, or generative, depending on the underlying architecture. Recent advances in LLMs have significantly improved assistant capabilities but raised challenges around hallucinations, security, and alignment with user intent.

Designing effective AI assistants involves balancing accuracy, speed, safety, and user experience—especially in high-stakes or professional environments.