Refactor and rename LLM prompt optimization example

Renamed the example directory from 'llm_prompt_optimazation' to 'llm_prompt_optimization' for correct spelling and consistency. Updated all example files, added HuggingFace dataset support, new configuration files, and improved documentation. The new example supports prompt evolution for any HuggingFace dataset with custom templates and cascading evaluation. Removed the old example files and replaced them with the new structure.

Author: codelion

README.md

@@ -288,12 +288,72 @@ prompt:
   num_top_programs: 3      # Performance examples
   num_diverse_programs: 2  # Creative inspiration
   include_artifacts: true  # Execution feedback
+  
+  # Template customization
+  template_dir: null               # Directory for custom prompt templates
+  use_template_stochasticity: true # Enable random variations in prompts
+  template_variations: {}          # Define variation placeholders
 ```
 
 Sample configuration files are available in the `configs/` directory:
 - `default_config.yaml`: Comprehensive configuration with all available options
 - `island_config_example.yaml`: Advanced island-based evolution setup
 
+### Template Customization
+
+OpenEvolve supports advanced prompt template customization to increase diversity in code evolution:
+
+#### Custom Templates with `template_dir`
+
+You can override the default prompt templates by providing custom ones:
+
+```yaml
+prompt:
+  template_dir: "path/to/your/templates"
+```
+
+Create `.txt` files in your template directory with these names:
+- `diff_user.txt` - Template for diff-based evolution
+- `full_rewrite_user.txt` - Template for full code rewrites  
+- `evolution_history.txt` - Format for presenting evolution history
+- `top_program.txt` - Format for top-performing programs
+- `previous_attempt.txt` - Format for previous attempts
+
+See these directories for complete examples of custom templates:
+- `examples/lm_eval/prompts/` - Custom templates for evaluation tasks
+- `examples/llm_prompt_optimization/templates/` - Templates for evolving prompts instead of code
+
+#### Template Variations with Stochasticity
+
+To add randomness to your prompts and prevent getting stuck in local optima:
+
+1. **Enable stochasticity** in your config:
+```yaml
+prompt:
+  use_template_stochasticity: true
+  template_variations:
+    greeting:
+      - "Let's improve this code."
+      - "Time to enhance this program."
+      - "Here's how we can optimize:"
+    analysis_intro:
+      - "Current metrics show"
+      - "Performance analysis indicates"
+      - "The evaluation reveals"
+```
+
+2. **Use variation placeholders** in your custom templates:
+```
+# custom_template.txt
+{greeting}
+{analysis_intro} the following results:
+{metrics}
+```
+
+The system will randomly select one variation for each placeholder during prompt generation, creating diverse prompts that can lead to more creative code evolutions.
+
+**Note**: The default templates don't include variation placeholders, so you'll need to create custom templates to use this feature effectively.
+
 ### Feature Dimensions in MAP-Elites
 
 Feature dimensions control how programs are organized in the MAP-Elites quality-diversity grid:
@@ -425,8 +485,12 @@ Demonstrates integration with [optillm](https://github.com/codelion/optillm) for
 - **Mixture of Agents (MoA)**: Multi-response synthesis for improved accuracy  
 - **Local model optimization**: Enhanced reasoning with smaller models
 
-#### [LLM Prompt Optimization](examples/llm_prompt_optimazation/)
-Evolving prompts themselves for better LLM performance, demonstrating self-improving AI systems.
+#### [LLM Prompt Optimization](examples/llm_prompt_optimization/)
+Evolving prompts for better LLM performance on HuggingFace datasets. Features:
+- Custom templates for evolving prompts instead of code
+- Two-stage cascading evaluation for efficiency
+- Support for any HuggingFace dataset
+- Automatic prompt improvement through evolution
 
 ### Systems & Performance Optimization