How API Description Languages Can Empower AI

AI and APIs are a match made in heaven. For one thing, APIs are AI’s nervous system, as they make up the infrastructure that connects users, software, and AI-driven tools like LLMs. APIs also act as a bridge between AI, websites, and software. That’s to say nothing about AI’s ability to empower the API development process itself.

There have been great strides in AI-driven APIs, especially when the AI knows what to expect. This is one of the reasons API description languages are so important for the future of AI, thanks to trends like code scaffolding. To learn more about this emerging area, let’s delve into how advances in API description languages are driving the future of AI.

7 Ways API Description Languages Empower AI

An API description language describes how an API behaves on a granular level. This makes them ideal for integrating with AI in many different ways. Description languages specify what sort of assets a component should expect, for example. It can also give a machine learning model a complete list of available endpoints. This is an essential part of making API components interchangeable.

1. Empowers Automated Code Generation

Even more importantly, API description languages make automated code generation possible. Many of these languages come with tools capable of generating client libraries, server stubs, and comprehensive documentation based on the API specification. This aspect promises to greatly improve both user experience (UX) and developer experience (DX), as it allows ready-to-use code to be generated for a specific API. This promises to be a huge deal not just for API developers and AI enthusiasts but for both industries as well. The ability to create and use APIs using natural language promises to be a game-changer for the API industry.

2. API Description Languages Add Context

APIs rarely operate in a vacuum. One of the primary reasons that APIs exist is to be used with other APIs, either as part of a workflow or to connect software, tooling, and other resources. Until recently, this has been a major oversight of the API industry, with developers having to figure out API integration by hand. What was once a minor inconvenience became a major problem with the advent of LLMs, which have a tendency to make up things it doesn’t understand.

This trend led to the development of the Arazzo Specification, an update of OAS that allows developers to specify workflows. This extension could provide LLMs with a much greater understanding of the role of each API in the stack.

3. Allows Access To More Up-To-Date Information

LLMs tend to be trained on large datasets that are quite a bit out of date, greatly limiting their usefulness and increasing the likelihood of inaccuracies. As AI gets more sophisticated, however, developers and data scientists are correcting this unfortunate tendency. For instance, new LLMs like Gorilla are emerging specifically for working with APIs. Gorilla was created to construct custom API calls, but it also interacts with API documentation so that an AI always works with the latest version. Not only does this make an AI more suitable for real-time problem solving, but it also improves LLM accuracy.

4. API Descriptions Foster Consistency

Consistent API design could also change the industry. Inconsistent APIs often lead to errors, frustrating users and developers alike. These errors are often caused by simple mistakes, like an improperly formatted endpoint or receiving the wrong sort of asset.

API description languages eliminate virtually all of these potential errors, particularly when AI is involved. They detail almost everything about an API, including a detailed breakdown of all endpoints, inputs, outputs, and authentication methods, making them predictable both for users and AI. This consistency is essential for any sort of widespread API adoption. Having to troubleshoot and debug code makes getting an API up and running much more time-consuming. API description languages play an important role in fostering more usable and consistent experiences.

Also read: Most APIs Suffer From Specification Drift 5. Natural Language Processing

Natural language processing (NLP) is an incredibly popular application for AI, and it doesn’t show any signs of slowing. Analysts predict that NLP will see a CAGR of over 15.1% between 2023 and 2030. API descriptions play an important function in NLP, as it requires a considerable amount of both structured and unstructured data to function. NLP generally draws upon a wide range of sources, including everything from documents to social media feeds. APIs are typically used to provide access to these sources.

API description languages can let NLP models know what to expect. For example, an AI-driven chatbot might look up a customer’s account to deliver better customer service. It might even interact with a server to give real-time updates and information about inventory stock and pricing. The API description language facilitates all that, as it lets the AI know precisely what to call on the backend. This allows a user-facing AI to perform many tasks without additional developer input.

6. Automated Decision Making

AI-driven decision-making systems rely heavily on APIs to access data, trigger actions, and make real-time decisions. These include common AI applications like recommendation engines and autonomous vehicles. APIs bind all of these services together. API description languages make them structured and predictable, allowing them to be linked together and used interchangeably without human intervention.

For another example, think of a financial trading AI that draws upon several different exchanges. API description languages specify how the AI should interact with each exchange’s data, including the required parameters, data formats, and expected responses. Best of all, new exchanges can effortlessly be integrated into the existing stack. For the autonomous vehicles we mentioned earlier, API description languages could allow the AI to communicate with external sensors, control systems, and cloud-based services. When new systems, controls, or services become available, they can be plugged into the existing system quickly and easily.

7. Improved UX and DX

Integrating API description languages and AI allows unprecedented ease for developing, using, and maintaining APIs. The clear and well-defined structure dictated by the description language makes creating, testing, and deploying new features and even entire APIs nearly effortless. An API developer might create an OpenAPI Specification (OAS) for a new API they’re hoping to build, for example. Once the OAS is complete, the developer might plug the description into an AI-driven tool to create the code to implement the API. This is sure to be a major blessing, considering that developers spend 30% of their time coding APIs.

API description languages are both human and machine-readable, which is another benefit. This allows developers to work from the same code as the AI. This greatly reduces the possibility of miscommunications, making the workflow much more efficient. This lets developers focus on more important tasks, boosting morale and productivity. Integrating API description languages and AI also lets developers create custom code snippets and test examples, so they can try out new APIs with virtually no effort.

Final Thoughts On API Description Languages and AI

The API economy has been blowing up and won’t be slowing down anytime soon, judging by the number of Postman Collections increasing from less than 500,000 to 46 million between 2016 and 2021. At that growth rate, API description languages will continue to play an important role in the API industry, especially in terms of empowering scalability and allowing developers with less technical expertise to implement their projects and realize their vision.

The future of APIs and the future of AI will be closely intertwined. The benefits work both ways. AI can boost productivity, increase developer morale, and help ensure API security, all of which will be vital for APIs’ growth and continued success.