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Models as Systems with Surrounding Components
Machine learning models, particularly LLMs, are not just standalone entities but are complex systems that involve additional components like reachable augmented generation and various tool use. These surrounding components play a significant role in influencing the behavior of the models during tests. Evaluating these tools separately can offer insights and potential for mixing and matching in different use cases, such as evaluating a rag tool independently based on existing evaluations of embedding quality which could correlate with its practical performance.