LLMs are used in a wide range of applications that involve understanding and generating text. One common use case is conversational AI, where models like GPT power chatbots and virtual assistants. These systems provide customer support, answer questions, or automate tasks, enhancing user experience across industries.
Another key use case is content generation, including writing articles, creating marketing copy, and generating code snippets. LLMs can also assist in summarizing documents, translating languages, or analyzing text for sentiment and trends. For example, a developer might use an LLM to generate summaries of technical reports or translate API documentation into multiple languages.
In software development, LLMs aid in tasks like code completion, bug detection, and documentation generation. Tools like GitHub Copilot demonstrate how LLMs can streamline coding workflows. Beyond these, LLMs are applied in search engines, recommendation systems, and even healthcare, where they analyze medical records or assist with research by summarizing scientific papers.