Product experimentation is a structured process used by product managers, growth marketers, and engineers to test new ideas, features, or changes in a controlled environment to measure their impact on user behavior and key metrics. Techniques such as A/B tests, multivariate tests, feature flags, and phased rollouts are employed to validate decisions with data before a full rollout, helping teams minimize risks and maximize insights. The process is hypothesis-driven, data-informed, and iterative, allowing teams to adapt and learn even from unsuccessful experiments. Successful product experimentation leads to faster decision-making, lower risk, and greater user engagement, ultimately improving product-market fit. Companies like Bolt have effectively integrated product experimentation into their strategies to optimize features and align product improvements with business outcomes. However, experimentation should be used judiciously, as it may not be suitable for trivial changes, small user bases, or early stages of product discovery. Product experimentation differs from A/B testing and user research, as it encompasses a broader range of testing methods and combines quantitative data with qualitative insights to understand both what works and why. A repeatable, scalable system is crucial for successful experimentation, and a modern experimentation stack includes analytics platforms, feature flagging tools, and qualitative research tools to execute, measure, and iterate effectively. Recent trends such as AI-generated hypotheses, auto-rollbacks, and sequential testing are shaping the future of product experimentation, making it a core capability for every product team.