#69 · Primary category: Prompt Engineering Tools

YiVal

ai ai-experiments ai-toolkit aigc api auto-prompting autogpt fine-tuning framework generative-ai gpt4 llm midjourney prompt prompt-engineering prompt-tuning promptengineering python stable-diffusion

Your Automatic Prompt Engineering Assistant for GenAI Applications

Project last updated:04/22/24

GitHub Stars

2.1K

Forks

329

Contributors

39

License

Apache-2.0

Why we included this project

Anyone who has stared at a mediocre model response and had no idea which words to change has felt the exact problem YiVal is built for. Instead of hand-editing prompts and guessing, it runs structured experiments that vary your prompt text, RAG retrieval settings, and model parameters against real evaluation metrics, then reports which combination actually performs best. Because the workflow is built around YAML configurations and reproducible runs, you can treat prompt tuning like a proper test suite rather than a series of hunches. That gives teams shipping LLM features a practical way to build confidence before launch, with evidence that a prompt is genuinely better and will hold up as models and data drift. The experiment results also surface the cost and latency of each configuration, so you can pick the leanest option that still meets your quality bar.

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