If you were to stop someone walking down the street and ask them how long artificial intelligence, or AI, has been a hot topic, they might say it’s something that’s emerged mostly in recent years. But AI has been around for a long time, with the term first being coined as long ago as 1955. Generative AI however is a different beast, and one that's largely responsible for moving the topic of AI to the tip of everyone’s tongues – from consumers to enterprises alike.

Back in August, SmartBear acquired Stoplight, a leader in API design and documentation. Since this acquisition, our focus has been on seamlessly integrating Stoplight's open-source tools—Spectral, Elements, and Prism—into SwaggerHub. Our goal is to simplify complex API development tasks and address the common challenges encountered in API development.

When comparing the types of APIs (application programming interfaces), API architecture types and different API categories are incredibly important. The following article will discuss API architecture and protocol types (REST and SOAP) and three types of API categories (open, internal, and partner). Here’s the key things to know about the types of APIs your organization might need: Table of Contents.

We all love the ease and affordability of SaaS (Software-as-a-Service) tools. We expect our SaaS apps to be available whenever we need them, building our business processes around this assumption. At WSO2, we are grateful that many organizations trust our SaaS solutions, and we continually work to ensure this trust is well-founded. It’s worth taking a moment to understand the impact when those mission-critical apps go offline. Just one minute of downtime can cost businesses thousands of dollars.

Organizations that monitor their LLMs will benefit from higher performing models at higher efficiency, while meeting ethical considerations like ensuring privacy and eliminating bias and toxicity. In this blog post, we bring the top LLM metrics we recommend measuring and when to use each one. In the end, we explain how to implement these metrics in your ML and gen AI pipelines.

The topic of testing AI and ensuring its responsibility, safety, and security has never been more urgent. Controversy and incidents of AI misuse have increased 26-fold since 2021, highlighting growing concerns. As users quickly find out, AI tools are not infallible; they can make mistakes, display overconfidence, and lack critical questioning. The reality of the market is that AI is prone to error. This is exactly why testing AI is crucial. But how do we test AI?