if you feel like you're not running enough experiments, the bottleneck is almost always infrastructure, not ideas. focus on improving your infrastructure: write parallelized code: many teams are still doing all their tests using for loops. spending 1-2 hours learning to write parallelized code can dramatically reduce your experimentation time, going from days to hours. build modular pipelines: if your entire rag application is a giant python file, it will be hard to test. but if each search index is a separate post request, you can test them individually. this allows you to focus on one component (like an image retriever system) and improve it from 30% to 80% accuracy in one afternoon. test locally when possible: create smaller synthetic datasets for quick iteration before running larger tests. the business impact: one client went from running 1 experiment per week to 5 experiments per day just by parallelizing their evaluation code. this 35x improvement in iteration speed directly translated to faster product improvements and shorter time-to-market. being able to test components in isolation is crucial for rapid experimentation. a lot of this comes down to good software engineering practices and thoughtful system design. key insight: fast experimentation infrastructure is a force multiplier. invest once in parallelization and modularity, then run 10x more experiments.