If you’ve worked with Large Language Models (LLMs), you’re likely familiar with this scenario: your team’s prompts are scattered across documents, spreadsheets, and different cloud consoles. Iterating is often a manual and inefficient process, making it difficult to track which changes actually improve performance.
To address this, we’re introducing LLM-Evalkit, a light-weight, open-source application designed to bring structure to this process. LLM-Evalkit is a practical lightweight framework built on Vertex AI SDKs using Google Cloud that centralizes and streamlines prompt engineering, enabling teams to track objective metrics and iterate more effectively.
Centralizing a disparate workflow
Currently, managing prompts on Google Cloud can involve juggling several tools. A developer might experiment in one console, save prompts in a separate document, and use another service for evaluation. This fragmentation leads to duplicated effort and makes it hard to establish a standardized evaluation process. Different team members might test prompts in slightly different ways, leading to inconsistent results.
LLM-Evalkit solves this by abstracting these disparate tools into a single, cohesive application. It provides a centralized hub for all prompt-related activities, from creation and testing to versioning and benchmarking. This unification simplifies the workflow, ensuring that all team members are working from the same playbook. With a shared interface, you can easily track the history and performance of different prompts over time, creating a reliable system of record.
Source Credit: https://cloud.google.com/blog/products/ai-machine-learning/introducing-llm-evalkit/
