Most tutorials show you how to call an LLM API. What they don't show you is what happens when that integration is processing 50,000 requests a day in a real enterprise environment, where downtime costs money and "it worked on my machine" isn't good enough.

This workshop is built entirely from production experience. I've spent the last two years embedding Claude and OpenAI into large-scale Laravel applications and I've made enough mistakes to save you from making your own. We'll go beyond the happy path and get into the messy, practical reality of running AI features at scale.

We'll work through how to structure LLM calls properly inside Laravel using queues and jobs, so your app doesn't grind to a halt waiting on a model response. We'll talk about prompt versioning, because models change, and if you're not managing that, your feature will quietly break in ways that are very hard to debug. We'll look at caching strategies that meaningfully cut API costs without sacrificing quality, and we'll cover observability: how to log, monitor, and alert on AI features the same way you would any other critical service.

We'll also spend time on graceful degradation, because the question isn't if an LLM call will fail, it's what your app does when it does.

By the end of the session, you'll have a set of patterns you can take back to your own Laravel codebase and start using straight away. No machine learning background needed, just solid PHP instincts and a willingness to get your hands dirty.