How to evaluate LLM apps
The crux of LLM deployment lies in the meticulous curation of training data to preempt biases, implementing human-led annotation for data enhancement, and establishing automated output monitoring systems. Evaluating LLMs either as standalone entities or in conjunction with an agent chain is crucial to ensure they function correctly and produce reliable results, and this is an integral part of the ML lifecycle. The evaluation process determines the performance of the models in terms of effectiveness, reliability, and efficiency.
The goal of evaluating LLMs is to understand their strengths and weaknesses, enhancing accuracy and efficiency while reducing errors, thereby maximizing their usefulness in solving real-world problems. This evaluation process typically occurs offline during the development phase. Offline evaluations provide initial insights into model performance under controlled test conditions and include aspects such as hyperparameter tuning...
