Large language models are rapidly moving beyond simple chat interfaces into practical, embedded applications. Two recent videos highlight how this shift is unfolding across very different use cases, from website customization to enterprise data workflows. Together, they show that the real value of AI lies in its ability to understand context, act on it, and integrate seamlessly into the tools people already use.
In a video from OpenAI, viewers learn how to build custom site annotations with ChatGPT. The demonstration shows how annotation controls and suggested prompts can be added directly to a website, allowing users to edit scenes and enrich expense records through context available to their agent. The approach relies on WebMCP, a framework that connects ChatGPT's capabilities to live web environments, turning passive browsing into an interactive, agent-assisted experience.
H2O.ai takes a broader view in a separate video, arguing that language models are far more than a chat technology. The same underlying capability powers conversational AI, document intelligence, and text-to-SQL. Conversational AI handles multi-turn dialogue, remembers context, adapts to intent, and escalates when limits are reached. Document extraction pulls structured data from contracts and invoices, while text-to-SQL lets users query databases in plain language. Together, these examples illustrate how LLMs are becoming versatile tools for both consumer
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