Building a Generative AI Evaluation Framework

Generative artificial intelligence (gen AI) is driving advancements in various industries, with adoption consistently increasing. However, evaluating gen AI performance for specific use cases presents challenges due to its complexity compared to traditional AI. Subjectivity, bias in datasets, scalability, and interpretability are some of the key issues. To address these challenges, experts can build a comprehensive evaluation pipeline by considering factors such as task type, data type, computational complexity, and need for model interpretability and observability. The steps to build an effective gen AI evaluation framework include defining the problem and objectives, establishing performance benchmarks, collecting and preprocessing relevant data, feature engineering, fine-tuning a foundation model, evaluating the model, and continuous monitoring. Encord Active is an AI-based evaluation platform that supports active learning pipelines for evaluating data quality and model performance in computer vision tasks.

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