PromptWizard

What is the project about?

PromptWizard is a framework for optimizing discrete prompts for Large Language Models (LLMs). It uses a self-evolving mechanism where the LLM generates, critiques, and refines its own prompts and examples.

What problem does it solve?

It addresses the challenge of manually crafting effective prompts for LLMs, which can be time-consuming and require significant expertise. It automates the process of prompt optimization, leading to improved task performance.

What are the features of the project?

• Feedback-driven Refinement: The LLM iteratively improves prompts and examples based on feedback.
• Critique and Synthesize diverse examples: Generates robust, diverse, and task-aware synthetic examples. Optimizes both the prompt instructions and the in-context learning examples.
• Self-generated Chain of Thought (CoT): Generates CoT steps with a combination of positive, negative, and synthetic examples.
• Iterative and Sequential Optimization: Supports both iterative optimization of instructions and sequential optimization of instructions and examples.
• Task Alignment: Integrates task intent and expert personas to align prompts with human reasoning.
• Multiple Scenarios: Supports optimizing prompts with or without training data, and generating synthetic examples.
• Dataset Support: Supports several datasets out-of-the-box (GSM8k, SVAMP, AQUARAT, Instruction Induction (BBII)) and provides instructions for custom datasets.

What are the technologies used in the project?

• Python
• Large Language Models (LLMs) accessed via API (Azure OpenAI and OpenAI API keys are supported).
• .jsonl file format for datasets.
• YAML for configuration.

What are the benefits of the project?

• Automated Prompt Optimization: Reduces manual effort in prompt engineering.
• Improved Task Performance: Leads to better results from LLMs.
• Continuous Improvement: The self-evolving mechanism ensures ongoing optimization.
• Robustness and Diversity: Synthetic examples enhance the robustness and diversity of prompts.
• Interpretability: Alignment with human reasoning improves interpretability.
• Adaptability: Can be used with various datasets and scenarios.

What are the use cases of the project?

• Improving the performance of LLMs on specific tasks.
• Automating the creation of high-quality prompts for various applications.
• Generating synthetic data for training LLMs.
• Researching and developing new prompt optimization techniques.
• Any task where an LLM's performance depends on the quality of the input prompt.