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AI Assistants Practical Implementations AI assistants can handle repetitive tasks and surface insights, but their value grows when they fit real workflows. Start by mapping a task from start to finish, decide what inputs the assistant needs, and define the expected outputs. Set guardrails and plan for a human handoff when needed. Common uses are simple but powerful: drafting replies and calendar invites, summarizing long documents, generating meeting agendas, and gathering data for short reports. In teams, assistants help keep onboarding checklists current and send gentle reminders for missing steps. In customer-facing work, they route inquiries and offer approved replies, escalating when necessary. ...

Language Models and Practical NLP Applications Language models are software systems that predict the next word or phrase in a sentence. They learn from large text data and can generate, summarize, translate, and reason about language. In everyday work, these models help with many tasks when you set clear goals and simple rules. They are not magic, but they can save time and support better decisions. What they do well ...