EDGE OSDEFINITION
LLM Evaluation (LLMs evals) are a structured way to evaluate how well your Large Language Models or AI applications are performing. You can do this by using a dataset, prompt template, answer template, scoring guide, automatically measuring or manually. This helps in several ways, which includes examining the issues faced by your model in analyzing the accuracy, safety and latency. LLM evaluation simplifies various problems with your models and can compare the other model efficiencies.
Why are LLM Evals important?
- Quality assurance: helps in identifying the minor to major inconsistencies in outputs and find irrelevant answers.
- Regression testing: Identify any changes in performance.
- Safety testing: Ensure answers meet your safety criteria.
- Cost/latency: Examine every change that impacts performance, positively or negatively.
- Model comparison: Ensure you are choosing the best model for your task.
Use cases
- Helps in finding the efficiency of your chatbot whether it is working correctly or not.
- Test hallucination rates in your RAG application.
- Compare prompts and models for document extraction.
- Ensure your AI generated content is relevant and following instructions.
- Monitor your AI performance after any changes to your model/system.
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