Perplexity trusts GPT-6 Astra with end-to-end systems
Perplexity trusts GPT-6 Astra with end-to-end systems
Perplexity 信任 GPT-6 Astra 处理端到端系统
September 14, 2026 2026年9月14日
Perplexity uses Astra to write communications, change software, and monitor production systems, and checks in much less frequently than with earlier models. Perplexity 使用 Astra 来撰写通讯、修改软件并监控生产系统，且相比早期模型，人工介入检查的频率大幅降低。
As an AI-powered answer engine, Perplexity is deeply focused on search and accuracy. Its ability to process large amounts of information is critically important. Johnny Ho, Cofounder and Chief Strategy Officer, observes that every time the model gets better at writing code, Perplexity’s search engine improves too. It becomes able to write better programs that search the web and internal information and summarize it very concisely. 作为一款人工智能驱动的答案引擎，Perplexity 高度关注搜索与准确性。其处理海量信息的能力至关重要。联合创始人兼首席战略官 Johnny Ho 指出，每当模型在编写代码方面取得进步，Perplexity 的搜索引擎也会随之提升。它能够编写出更优秀的程序，从而更高效地搜索网络及内部信息，并进行极其简洁的总结。
But the real challenge, according to Johnny, is taking those informational aspects and applying them to real-world systems. Something made easier with GPT‑6 Astra. 但 Johnny 认为，真正的挑战在于如何将这些信息处理能力应用到现实世界的系统中。而 GPT-6 Astra 让这一切变得更加简单。
“We can have the model craft communications, edit real-world systems, and monitor our production software in a way that previous generations were not able to.” —Johnny Ho, Cofounder and Chief Strategy Officer, Perplexity “我们可以让模型撰写通讯、编辑现实系统并监控我们的生产软件，这是以往几代模型所无法做到的。” ——Johnny Ho，Perplexity 联合创始人兼首席战略官
Letting the model do the testing 让模型进行测试
For Johnny, one of the most useful applications of AI is testing code. With limited time to test manually, he asks GPT‑6 Astra to build a small testing program around an application. 对 Johnny 而言，人工智能最有用的应用之一就是测试代码。由于手动测试的时间有限，他会要求 GPT-6 Astra 围绕应用程序构建一个小型的测试程序。
The model generates realistic responses like those another service would send, for example, a language model API or a connector. By standing in for those services, the model can check how the application responds and test the workflow from start to finish. 该模型能够生成逼真的响应，就像其他服务（例如语言模型 API 或连接器）所发送的内容一样。通过模拟这些服务，模型可以检查应用程序的响应情况，并对整个工作流程进行端到端的测试。
“We’re actually able to trust it with full end-to-end systems and check in on it much less frequently than previous generations of models.” —Johnny Ho, Cofounder and Chief Strategy Officer, Perplexity “我们现在确实能够放心地将完整的端到端系统交给它处理，并且相比以往的模型，我们检查它的频率也大大降低了。” ——Johnny Ho，Perplexity 联合创始人兼首席战略官