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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.
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.
“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.”
Letting the model do the testing
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.
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.
“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.”