Continuous Integration and Continuous Delivery, adapted to AI systems. In 2026, AI-specific CI/CD pipelines run prompt regression tests, eval suites, safety
CI/CD automates the path from code change to production: continuous integration builds and tests every commit; continuous delivery/deployment ships validated changes through staged environments with rollback. For AI systems the pipeline extends to data, prompts, and models: eval suites gate releases the way unit tests gate code.
AI behavior changes through many doors, code, prompts, models, retrieval data, and unpiped changes are how regressions reach users. CI/CD with eval gates is the discipline that lets teams ship AI improvements weekly without praying: every change tested, every release reversible.
Eval gates (task-quality test suites as release criteria), versioning across prompts/models/data, not just code, and staged rollouts with behavioral monitoring, because AI regressions are often statistical rather than binary failures.
Score distributions, not exact matches: run eval sets with thresholds and confidence intervals, pin seeds/temperature where possible, and trend results across runs. The gate is 'quality didn't drop significantly,' not 'output identical.'
Yes: prompts are behavior-defining production artifacts. Untested prompt edits are the most common cause of silent AI regressions; route them through review, evals, and versioned deploys like any code.