Lilypad: Effective Prompt Engineering Starts With Automatic Versioning And Tracing

Learn how Lilypad automatically versions and traces prompts, enabling systematic data collection, experiment tracking, and reproducible validation for effective prompt engineering.

Overview

Prompt engineering is a non-deterministic ML problem that needs systematic validation and refinement to produce anything of quality. This requires properly collecting data and tracking experiments against which you can validate changes and results.

Automatically versioning and tracing every prompt creates the proper foundation for a data-driven approach to prompt engineering, and Lilypad makes taking this first step as simple as it can be.

Links

https://www.lilypad.so
https://github.com/Mirascope/lilypad
Open-source framework for versioning, tracing, and evaluating non-deterministic LLM function calls.

Tech stack