keirp/automatic_prompt_engineer
Automatic Prompt Engineer
Automatic Prompt Engineer is a framework designed to automate the generation, refinement, and performance measurement of language model instructions. It functions as a systematic tool for optimizing prompt phrasing by iteratively testing candidate instructions against specific input and output datasets to maximize task accuracy.
The system distinguishes itself through an evaluation-driven approach that uses automated feedback loops to score prompt variations. By employing template-based input structuring, it ensures consistent testing environments where candidate instructions are measured against predefined performance metrics.
The framework includes integrated utilities for managing the computational and financial resources required for optimization tasks. It provides pre-computation features to estimate token usage and costs before execution, allowing for budget control during large-scale testing. Users can define custom evaluation and prompt templates to standardize how models interact with data and how performance is scored across different iterations.
Features
- Automated Prompt Engineering - Provides a systematic framework for the automated generation, refinement, and performance measurement of language model instructions.
- Automated Prompt Optimization - Automates the refinement and selection of model instructions by evaluating candidate prompts against target datasets.
- Prompt Optimization Frameworks - Provides a comprehensive framework for automatically generating, evaluating, and refining natural language instructions based on performance data.
- Prompt Optimization Tools - Automatically refines natural language instructions by iteratively generating, evaluating, and selecting the most effective phrasing.
- Prompt Evaluation Tools - Ranks and selects optimal prompt variations by measuring their performance against predefined input and output datasets.
- Large Language Models - Facilitates the iterative engineering of natural language instructions to maximize language model performance on specific tasks.
- Automated Code Refinement Loops - Implements automated loops that iteratively modify instructions based on performance feedback to improve model outcomes.
- Prompt Iteration Workflows - Provides workflows for the iterative generation and testing of prompts to identify the most effective phrasing.
- LLM Evaluation - Standardizes the evaluation of language model workflows by scoring performance across different prompt variations.
