Highlights §
- Auto Prompt is a prompt optimization framework designed to enhance and perfect your prompts for real-world use cases. (View Highlight)
- The framework automatically generates high-quality, detailed prompts tailored to user intentions. It employs a refinement (calibration) process, where it iteratively builds a dataset of challenging edge cases and optimizes the prompt accordingly. This approach not only reduces manual effort in prompt engineering but also effectively addresses common issues such as prompt sensitivity and inherent prompt ambiguity issues. (View Highlight)
- Our mission: Empower users to produce high-quality robust prompts using the power of large language models (LLMs). (View Highlight)
- Prompt Engineering Challenges. The quality of LLMs greatly depends on the prompts used. Even minor changes can significantly affect their performance. (View Highlight)
- Benchmarking Challenges. Creating a benchmark for production-grade prompts is often labour-intensive and time-consuming. (View Highlight)
- Reliable Prompts. Auto Prompt generates robust high-quality prompts, offering measured accuracy and performance enhancement using minimal data and annotation steps. (View Highlight)
- Modularity and Adaptability. With modularity at its core, Auto Prompt integrates seamlessly with popular open-source tools such as LangChain, Wandb, and Argilla, and can be adapted for a variety of tasks, including data synthesis and prompt migration. (View Highlight)
- The system is designed for real-world scenarios, such as moderation tasks, which are often challenged by imbalanced data distributions. The system implements the Intent-based Prompt Calibration method. The process begins with a user-provided initial prompt and task description, optionally including user examples. The refinement process iteratively generates diverse samples, annotates them via user/LLM, and evaluates prompt performance, after which an LLM suggests an improved prompt. (View Highlight)
- The optimization process can be extended to content generation tasks by first devising a ranker prompt and then performing the prompt optimization with this learned ranker. The optimization concludes upon reaching the budget or iteration limit. (View Highlight)
- This joint synthetic data generation and prompt optimization approach outperform traditional methods while requiring minimal data and iterations. Learn more in our paper Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases by E. Levi et al. (2024). (View Highlight)
- Using GPT-4 Turbo, this optimization typically completes in just a few minutes at a cost of under $1. To manage costs associated with GPT-4 LLM’s token usage, the framework enables users to set a budget limit for optimization, in USD or token count, configured as illustrated here. (View Highlight)
- Features
• 📝 Boosts prompt quality with a minimal amount of data and annotation steps.
• 🛬 Designed for production use cases like moderation, multi-label classification, and content generation.
• ⚙️ Enables seamless migrating of prompts across model versions or LLM providers.
• 🎓 Supports prompt squeezing. Combine multiple rules into a single efficient prompt. (View Highlight)