autoresearch

To automate an experimentation loop, you need a project with tunable variables and an evaluation script that prints metrics to stdout. The autoresearch loop modifies code, runs evaluations, measures results, and keeps improvements or reverts regressions autonomously using Git checkpoints.

Autonomous experimentation for prompt engineering optimization is an iterative process where a system modifies prompts, executes evaluations, and measures metrics to find optimal configurations automatically. It manages decision-making to keep improvements and revert regressions without manual intervention.

Yes, you need Git to run autonomous optimization loops. Git is required for checkpointing during the experimentation process, allowing the system to revert regressions and keep improvements by restoring previous states when measured metrics do not improve.

Yes, you can use autonomous experimentation to optimize landing page performance metrics. The loop supports versatile applications including landing pages, RAG pipelines, API performance, and ML training, automatically adjusting variables and measuring results to improve systems.

The best way to set up an autonomous experiment loop for API performance tuning is to provide an evaluation script that outputs metrics to stdout and use an agent to initiate the autoresearch loop, which will iteratively modify configurations and measure API improvements.

The autoresearch loop measures results by executing an evaluation script that prints metrics to stdout. It compares these measured metrics against previous iterations, using Git checkpointing to keep modifications that yield improvements and revert changes that cause regressions.