Prior to releasing any new system we conduct rigorous testing, engage external experts for feedback, work to improve the model's behavior with techniques like reinforcement learning with human feedback, and build broad safety and monitoring systems.

For example, after our latest model, GPT‑4, finished training, we spent more than 6 months working across the organization to make it safer and more aligned prior to releasing it publicly.

We believe that powerful AI systems should be subject to rigorous safety evaluations. Regulation is needed to ensure that such practices are adopted, and we actively engage with governments on the best form such regulation could take.

We work hard to prevent foreseeable risks before deployment, however, there is a limit to what we can learn in a lab⁠. Despite extensive research and testing, we cannot predict all of the beneficial ways people will use our technology⁠, nor all the ways people will abuse it. That’s why we believe that learning from real-world use is a critical component of creating and releasing increasingly safe AI systems over time.