LLM Eval Workflow: How to Build Reliable AI Quality Gates Without Vibes

1. 1Build a test dataset for LLM evaluation using relevant tools and frameworks
2. 2Configure metrics for LLM performance evaluation, such as accuracy and F1 score
3. 3Run automated tests to compare LLM performance before and after updates
4. 4Apply statistical methods to determine significant improvements in LLM performance
5. 5Test and refine the evaluation workflow to ensure reliability and consistency

Developers and AI engineers can benefit from this workflow to ensure AI features are improved before shipping, and product managers can use it to make informed decisions about AI feature deployment

💡 A well-designed evaluation workflow is crucial for ensuring AI features are improved before shipping

🚀 Build reliable AI quality gates for LLMs with a practical evaluation workflow! 📈