fix: Parse LLM design variable bounds correctly and save workflow config

CRITICAL FIXES:

1. Parameter Range Parsing Bug
   - LLM returns bounds as [min, max] array, but code was looking for 'min'/'max' keys
   - This caused all parameters to default to 0-1 range instead of actual mm values
   - Example: "20 to 30 mm" was being used as 0.2-1.0mm instead of 20-30mm

2. Missing Workflow Documentation
   - Added automatic saving of LLM workflow config to output directory
   - Creates llm_workflow_config.json with complete optimization setup
   - Includes design variables, bounds, objectives, constraints, engineering features

Changes:
- optimization_engine/llm_optimization_runner.py:
  * Lines 205-211: Parse 'bounds' array from LLM output
  * Lines 80-84: Save workflow config JSON for transparency
  * Maintains backward compatibility with old 'min'/'max' format

Test Results:
BEFORE:
- beam_half_core_thickness: 0.27-0.95mm (WRONG!)
- beam_face_thickness: 0.07-0.73mm (WRONG!)

AFTER:
- beam_half_core_thickness: 20.16-28.16mm (CORRECT!)
- beam_face_thickness: 21.69-24.73mm (CORRECT!)

E2E test now passes with realistic parameter values and proper documentation.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

optimization_engine/llm_optimization_runner.py

@@ -77,6 +77,12 @@ class LLMOptimizationRunner:
         self.output_dir = Path(output_dir)
         self.output_dir.mkdir(parents=True, exist_ok=True)
 
+        # Save LLM workflow configuration for transparency and documentation
+        workflow_config_file = self.output_dir / "llm_workflow_config.json"
+        with open(workflow_config_file, 'w') as f:
+            json.dump(llm_workflow, f, indent=2)
+        logger.info(f"LLM workflow configuration saved to: {workflow_config_file}")
+
         # Initialize automation components
         self._initialize_automation()
 
@@ -201,8 +207,14 @@ class LLMOptimizationRunner:
         design_vars = {}
         for var_config in design_vars_config:
             var_name = var_config['parameter']
-            var_min = var_config.get('min', 0.0)
-            var_max = var_config.get('max', 1.0)
+
+            # Parse bounds - LLM returns 'bounds' as [min, max]
+            if 'bounds' in var_config:
+                var_min, var_max = var_config['bounds']
+            else:
+                # Fallback to old format
+                var_min = var_config.get('min', 0.0)
+                var_max = var_config.get('max', 1.0)
 
             # Suggest value using Optuna
             design_vars[var_name] = trial.suggest_float(var_name, var_min, var_max)