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* feat: Parallelize prompt evaluation in optimize-prompt.py - Update `PromptOptimizer.evaluate_prompt` to use `ThreadPoolExecutor` for concurrent test case processing - Significantly reduces total execution time when using high-latency LLM clients (network IO bound) - Maintain accurate metric aggregation (latency, accuracy, token count) from parallel results - This prepares the script for real-world usage where sequential execution is a major bottleneck ⚡ Bolt: Reduces total evaluation time from O(n) to O(1) latency-wise (bounded by max_workers) for concurrent requests. * feat: Parallelize prompt evaluation in optimize-prompt.py - Update `PromptOptimizer.evaluate_prompt` to use `ThreadPoolExecutor` for concurrent test case processing - Significantly reduces total execution time when using high-latency LLM clients (network IO bound) - Maintain accurate metric aggregation (latency, accuracy, token count) from parallel results - Ensure no generated artifacts (`optimization_results.json`) are committed ⚡ Bolt: Reduces total evaluation time from O(n) to O(1) latency-wise (bounded by max_workers) for concurrent requests. --------- Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>