Techniques for Self-Improving LLM Evals

Self-improving LLM evals involve creating robust evaluation pipelines for AI applications. The process includes curating a dataset of relevant examples, determining evaluation criteria using LLMs, refining prompts with human annotations, and fine-tuning the evaluation model. By following these steps, LLM evaluations can become more accurate and provide deeper insights into the strengths and weaknesses of the models being assessed.