From a86384334b75c01280e2993af1f3618c1e92a3b4 Mon Sep 17 00:00:00 2001
From: "google-labs-jules[bot]"
 <161369871+google-labs-jules[bot]@users.noreply.github.com>
Date: Sun, 21 Dec 2025 19:02:37 -0500
Subject: [PATCH] =?UTF-8?q?=E2=9A=A1=20Bolt:=20optimize=20prompt=20evaluat?=
 =?UTF-8?q?ion=20loop=20to=20skip=20redundant=20calls=20(#152)?=
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- Avoid re-evaluating the current prompt if metrics are already available from the previous iteration.
- Pass metrics from the best variation to the next iteration.
- Reduces N-1 expensive LLM calls in an N-iteration optimization loop.

Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
---
 .../scripts/optimize-prompt.py                        | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/plugins/llm-application-dev/skills/prompt-engineering-patterns/scripts/optimize-prompt.py b/plugins/llm-application-dev/skills/prompt-engineering-patterns/scripts/optimize-prompt.py
index 97159f8..5357b6c 100644
--- a/plugins/llm-application-dev/skills/prompt-engineering-patterns/scripts/optimize-prompt.py
+++ b/plugins/llm-application-dev/skills/prompt-engineering-patterns/scripts/optimize-prompt.py
@@ -106,12 +106,18 @@ class PromptOptimizer:
         current_prompt = base_prompt
         best_prompt = base_prompt
         best_score = 0
+        current_metrics = None
 
         for iteration in range(max_iterations):
             print(f"\nIteration {iteration + 1}/{max_iterations}")
 
             # Evaluate current prompt
-            metrics = self.evaluate_prompt(current_prompt)
+            # Bolt Optimization: Avoid re-evaluating if we already have metrics from previous iteration
+            if current_metrics:
+                metrics = current_metrics
+            else:
+                metrics = self.evaluate_prompt(current_prompt)
+
             print(f"Accuracy: {metrics['avg_accuracy']:.2f}, Latency: {metrics['avg_latency']:.2f}s")
 
             # Track results
@@ -137,14 +143,17 @@ class PromptOptimizer:
             # Test variations and pick best
             best_variation = current_prompt
             best_variation_score = metrics['avg_accuracy']
+            best_variation_metrics = metrics
 
             for variation in variations:
                 var_metrics = self.evaluate_prompt(variation)
                 if var_metrics['avg_accuracy'] > best_variation_score:
                     best_variation_score = var_metrics['avg_accuracy']
                     best_variation = variation
+                    best_variation_metrics = var_metrics
 
             current_prompt = best_variation
+            current_metrics = best_variation_metrics
 
         return {
             'best_prompt': best_prompt,