How to Add LLM Evaluations to CI/CD Pipelines

Continuous Integration and Continuous Deployment (CI/CD) pipelines can be used to evaluate large language models (LLMs) effectively by integrating LLM evaluations into your CI/CD pipelines, ensuring consistent and reliable AI performance and automating experimental results from your AI applications. To set up a CI/CD pipeline for LLM evaluations, you need to create a dataset of test cases, define tasks that represent the work your system is doing, create evaluators to measure outputs, run experiments, and add a yml file to prepare your script as for CI/CD. Best practices include automating LLM evaluation in CI/CD pipelines, combining quantitative and qualitative evaluations, using version control for models, data, and CI/CD configurations, and leveraging tools like Arize Phoenix to improve reliability and observability.

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