This is an introductory segment from a YouTube course on automated testing for LLMOps (ML operations for large language models), produced in partnership with CircleCI and taught by Rob Zuber. It emphasizes integrating automated tests into CI pipelines to catch regressions and unpredictable model behavior, using rule-based checks and model-graded evaluations.
Hi, I'm excited to introduce automated testing for LLMOPS, built in partnership with CircleCI and taught by Ralph Zuber. When building applications with generative AI, the model behavior can be much more open ended or more unpredictable than with traditional software, so systematic testing becomes even more important. Say you're using an LLM for writing content and one of your teammates makes an innocent update to the prompt to tell the LLM to make the content interesting but unknown to anyone until it's shipped. This makes the LLM create interesting content by hallucinating. But what if you had designed automated tests that are triggered to run after each time a teammate commits a code update? These tests are sometimes called regression tests, so when your team deploys the app you can be more confident that it works as expected because it's already been run through the common scenarios that we see in production. In this course, you integrate automated testing into your development process. You implement rule based evaluations such as string and pattern matching as well as model graded evaluations in which you prompt an evaluation LLM to check the outputs of the application LLM. I'm delighted that the instructor for this course is Rob Zuber, Chief technology officer at CircleCI. Rob has spent many years leading technology teams and also regularly shares his engineering insights at conferences and on CircleCI's podcast, the confident Commit.
Thanks Andrew. You will automate these tests using a continuous integration pipeline. Continuous integration, or CI for short, is the practice of making small changes to software and thoroughly testing those changes so that you can catch issues early when they're easier to fix. With a CI pipeline, whenever you make changes, this automatically triggers your set of LLM evaluations so that you can quickly identify and address any bugs and unexpected behaviors. Automated testing frees you up as the developer to focus your time and creativity on building exciting new features for your users.
Automated testing is a key component of LLMOPS or MLOPS, that is machine learning operations specialized to LLMs. Rob will walk you through the design and automation of various tests and also share when it helps to trigger different types of evaluations. And so if there's a takeaway I hope you get from this course, it is that.
Testing 1, 2, 3 testing. Testing exactly.
