Language models today, while useful for a variety of tasks, are still limited. The only information they can learn from is their training data. This information can be out-of-date and is one-size fits all across applications. Furthermore, the only thing language models can do out-of-the-box is emit text. This text can contain useful instructions, but to actually follow these instructions you need another process.

Though not a perfect analogy, plugins can be “eyes and ears” for language models, giving them access to information that is too recent, too personal, or too specific to be included in the training data. In response to a user’s explicit request, plugins can also enable language models to perform safe, constrained actions on their behalf, increasing the usefulness of the system overall.

We expect that open standards will emerge to unify the ways in which applications expose an AI-facing interface. We are working on an early attempt at what such a standard might look like, and we’re looking for feedback from developers interested in building with us.

Today, we’re beginning to gradually enable existing plugins from our early collaborators for ChatGPT users, beginning with ChatGPT Plus subscribers. We’re also beginning to roll out the ability for developers to create their own plugins for ChatGPT.

In the coming months, as we learn from deployment and continue to improve our safety systems, we’ll iterate on this protocol, and we plan to enable developers using OpenAI models to integrate plugins into their own applications beyond ChatGPT.

Connecting language models to external tools introduces new opportunities as well as significant new risks⁠(opens in a new window).

Plugins offer the potential to tackle various challenges associated with large language models, including “hallucinations,” keeping up with recent events, and accessing (with permission) proprietary information sources. By integrating explicit access to external data—such as up-to-date information online, code-based calculations, or custom plugin-retrieved information—language models can strengthen their responses with evidence-based references.

These references not only enhance the model’s utility but also enable users to assess the trustworthiness of the model’s output and double-check its accuracy, potentially mitigating risks related to overreliance as discussed in our recent GPT‑4 system card⁠(opens in a new window). Lastly, the value of plugins may go well beyond addressing existing limitations by helping users with a variety of new use cases, ranging from browsing product catalogs to booking flights or ordering food.

At the same time, there’s a risk that plugins could increase safety challenges by taking harmful or unintended actions, increasing the capabilities of bad actors who would defraud, mislead, or abuse others. By increasing the range of possible applications, plugins may raise the risk of negative consequences from mistaken or misaligned actions taken by the model in new domains. From day one, these factors have guided the development of our plugin platform, and we have implemented several safeguards.