Before the Raygun API limited release last year, we'd been consistently receiving requests for a public API for a long time, to provide a way for our customers to access their Raygun data programmatically. We're now proud to say we're providing a public API with a range of endpoints, but it took us a lot of planning and development to get here! In this post, I'd like to take you back to the beginning of development on our big API project. Specifically, I want to walk through the pivotal decisions we made around testing when we started development on the project, and how (and why) these have paid off.

Large Language Models (LLMs) like GPT-3 have revolutionized the field of artificial intelligence, offering unprecedented capabilities in natural language processing. Fine-tuning these models to specific tasks or datasets can enhance their performance. However, this process presents unique challenges and limitations that must be addressed. This article explores the intricacies of LLM fine-tuning, shedding light on the obstacles and constraints faced in this advanced AI domain.

In the expanding world of APIs, meaningful error responses can be just as important as well-structured success responses. In this post, I'll take you through some of the different options for creating responses that I've encountered during my time working at Raygun. We'll go over the pros and cons of some common options, and end with what I consider to be one of the best choices when it comes to API design, the Result Pattern. This pattern can lead to an API that will cleanly handle error states and easily allow for consistent future endpoint development.