YiVal is an automated prompt engineering assistant designed for GenAI applications, targeting developers and researchers seeking to optimize prompts, configurations, and model parameters. It offers a data-driven, evaluation-centric approach to enhance GenAI app performance, reduce latency, and minimize inference costs.

How It Works

YiVal employs a modular pipeline for prompt optimization, encompassing data generation, prompt variation, evaluation, selection, and enhancement. It leverages LLMs for generating test data and evaluating prompt performance, integrating with various metrics like BERTScore and AlpacaEval. The tool supports advanced techniques such as Chain of Density for summarization and LoRA for fine-tuning, enabling systematic improvement of GenAI outputs.

Quick Start & Requirements

• Installation: pip install yival or via Docker (docker pull yival/release:latest).
• Prerequisites: Python 3.10+, OpenAI API Key (as OPENAI_API_KEY environment variable). GPU support available via yival/release:cu12_torch_jupyter Docker image.
• Setup: Quickstart guide and demo videos are available.

Highlighted Details

• Supports multi-modal input (text and images) with RLHF/RLAIF.
• Evaluates and compares over 100 LLMs using custom datasets via LiteLLM integration.
• Implements advanced prompt engineering techniques like Chain of Density and "Large Language Models as Optimizers."
• Includes modules for LoRA fine-tuning and RAG configuration optimization.

Maintenance & Community

The project is sponsored by the Discord AIGC community. Contribution guidelines and a Discord channel are provided for community engagement and support.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README.

Limitations & Caveats

The README mentions that guardrails can increase latency and cost, and their performance in production is still a work in progress. Specific compatibility notes for commercial use or closed-source linking are not detailed.