Agentic AI: On Evaluations

Evaluating agentic AI applications requires a shift from traditional NLP metrics like BLEU and ROUGE to more nuanced approaches. A key modern technique involves using a powerful LLM as a "judge" to score the performance of other models on complex, open-ended tasks. For specific applications like multi-turn chatbots, important metrics include relevancy, knowledge retention, and role adherence. When evaluating Retrieval Augmented Generation (RAG) systems, it's crucial to measure the retrieval and generation components separately. The piece also introduces frameworks such as DeepEval and RAGAS that help implement these evaluation strategies.