microsoft/PromptWizard
PromptWizard
PromptWizard is an automated prompt engineering framework designed to evolve natural language instructions for generative tasks. It functions as an in-context learning optimizer and synthetic data generator, using mutation rounds and performance metrics to iteratively refine large language model instructions.
The system employs a self-reflective optimization loop that uses model-generated critiques to rewrite prompts. It distinguishes itself through the use of reasoning chain integration and persona-based prompting to steer the tone and professional quality of model responses.
The framework covers a broad range of prompt engineering capabilities, including the curation of few-shot examples and the generation of synthetic training data to address performance gaps. It also includes utilities for custom dataset integration to evaluate and measure the effectiveness of prompts against specific tasks.
Features
- Prompt Optimization Strategies - Iteratively refines large language model instructions through automated feedback loops to improve output quality and accuracy.
- Automated Prompt Engineering - Systematically evolves natural language instructions using automated critiques and mutation rounds instead of manual trial and error.
- Evaluator-Optimizer Loops - Employs an iterative evaluator-optimizer loop where the model critiques its own prompts to refine natural language outputs.
- Few-Shot Learning Optimizers - Optimizes in-context learning by automatically selecting and polishing few-shot examples and reasoning chains.
- LLM Performance Evaluators - Uses custom datasets and performance metrics to measure and improve the effectiveness of generative model instructions.
- Prompt Iteration Workflows - Refines prompt instructions through an iterative process of generation, critique, and mutation based on feedback.
- Prompt Optimization Frameworks - Implements a system for iteratively refining model instructions through automated feedback loops and self-reflective critique.
- Synthetic Data Generators - Ships an automated pipeline for producing synthetic training examples to fill performance gaps in model behavior.
- Synthetic Instruction Generators - Creates artificial training examples based on identified prompt weaknesses to iteratively improve instruction accuracy.
- Synthetic Dataset Generation - Creates artificial training examples to address performance gaps and expand datasets for improving model behavior.
- Chain-of-Thought Prompting - Integrates step-by-step reasoning sequences into prompts to enhance problem-solving capacity and interpretability.
- Custom Dataset Evaluators - Processes user-provided datasets of question-answer pairs to evaluate and optimize prompt effectiveness.
- Example Ranking and Selection - Provides mechanisms for collecting and ranking high-quality few-shot examples to guide model output patterns.
- Role-Based Prompting - Assigns expert personas and roles to steer the tone and professional quality of generated responses.
- Prompt Engineering - Task-aware framework for prompt optimization.
Star history
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