AI & LLM APIs
A few years ago, nobody’s API toolkit needed to think about REST, GraphQL, and WebSockets all at once, and then it did, because that is just what modern development looks like now. The same shift is happening again, faster this time, and it is called MCP. If your toolkit only…
You have swapped in the new model, updated the endpoint, and now you just need to know one thing: does the request actually return what you expect. Not eventually, not after you dig out your laptop, but right now, from wherever you happen to be. That is the entire premise…
A year ago, testing an AI endpoint meant pasting a cURL command into a terminal and parsing the response manually. Today, developers are hitting OpenAI, Anthropic, Google Gemini, Mistral, and a growing list of open-source model APIs dozens of times a day. They are tweaking system prompts, validating structured outputs,…
Not long ago, testing an API was a relatively predictable exercise. You sent a request, you got a response, and you checked whether the response matched what the documentation said it should be. Pass or fail. The logic was deterministic, the outputs were consistent, and your test suite could be…
There’s a version of this conversation happening in every engineering team right now. Someone opens a ticket, mentions that AI can write test cases automatically, and suddenly everyone has an opinion. Proponents point to faster coverage, fewer manual steps, and tools that practically think alongside you. Skeptics push back: APIs…
A few years ago, writing an API test meant sitting down, reading through documentation, mapping out expected responses, and manually crafting requests one by one. It was slow, it was repetitive, and for teams moving fast, it was often the first thing that got cut when deadlines tightened. That’s changing.…
