mshumer/gpt-prompt-engineer

Features

• Automated Prompt Engineering - Provides an automated system to create, test, and refine prompts using language models to optimize output quality.
• Iterative Refinement Workflows - Uses recursive loops and LLM analysis to iteratively rewrite prompt instructions for better quality.
• Prompt Evaluation Tools - Compares different prompt variations using rating systems and correctness matching to identify optimal instructions.
• Automated Prompt Optimization - Automates the iterative refinement of model instructions and examples based on performance metrics.
• Classification Tuning Tools - Implements tools for refining classification accuracy by comparing model outputs against ground-truth values.
• ELO-Based Ranking Systems - Implements a head-to-head competition framework using ELO ratings to determine the most effective prompt.
• Model Capability Extensions - Generates gold-standard examples from high-capability models to enhance the functional output of smaller models.
• Model Distillation Frameworks - Creates high-quality example pairs from frontier models to serve as training data for smaller models.
• Model Distillation Pipelines - Generates gold-standard training datasets using frontier models to distill knowledge into smaller models.
• Model Distillation Tools - Provides a utility for transferring knowledge from teacher models to student models via high-quality example pairs.
• Prompt Optimizers - Condenses expansive prompts into streamlined versions for smaller models while maintaining effectiveness.
• Model Performance Evaluators - Quantifies prompt accuracy by comparing model outputs against predefined ground-truth labels.
• Prompt Engineering Frameworks - Provides a structured framework for generating candidate prompts and ranking them via correctness matching.
• Prompt Variation Generators - Generates multiple candidate prompt versions based on use-case descriptions for comparative evaluation.
• Prompt Optimization - Iteratively improves prompt structure and wording to increase the quality of model outputs.
• Prompt Iteration - Analyzes performance failures to rewrite instructions and improve accuracy for classification tasks.
• Prompt Version Trackers - Associates model outputs with specific prompt versions to rank performance across a test suite.
• Automated Prompt Testing - Ranks candidate prompts through head-to-head competitions across test cases to identify the top performer.
• Prompt Benchmarking Suites - Features a benchmarking suite for evaluating and ranking different prompt versions using test cases.
• Test Case Definitions - Evaluates prompt performance by validating outputs against structured test case definitions and expected outcomes.
• Classification Metrics - Measures prompt effectiveness for classification tasks by calculating accuracy against expected true or false values.
• Experiment Logging - Records prompt versions and performance metrics to track quality improvements over time.
• Experiment Tracking - Records prompt configurations and performance metrics to analyze how wording changes affect model results.
• Format Adapters - Transforms instruction-tuned prompts into completion-based patterns for base model compatibility.
• Base Model Pattern Converters - Converts structured chat-style instructions into pattern-based completions compatible with base language models.
• Autonomous Agents - Automated tool for generating, testing, and ranking prompts.
• Autonomous AI Agents - Automated system for generating, testing, and ranking prompts.

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