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Systematic Evaluation is Integral to AI Development
In building large language models, systematic evaluation is essential for addressing failures and ensuring functionality. Initial attempts to refine outputs often involve prompt engineering, which quickly becomes unmanageable and can introduce new errors. Just as unit tests are vital in software engineering, evaluations are crucial for AI systems, allowing developers to track changes and their impacts effectively. The emphasis should be on establishing a robust evaluation process rather than solely relying on tools, which may facilitate but cannot replace a thoughtful approach to assessments. Effective evaluations require comprehensive data analysis and minimizing barriers to data access.