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How to Ensure Your AI Solution Does What You Expect iI to Do
- Generative AI (GenAI) is evolving rapidly, focusing on creating real value in 2025 by integrating AI into products and processes to enhance user experience, efficiency, competitiveness, and growth.
- While APIs and pre-trained models make GenAI integration easier, ensuring AI solutions work as intended post-deployment is crucial.
- GenAI introduces unpredictability compared to traditional software and classical machine learning, requiring evaluations to ascertain system performance.
- Evaluations measure the quality, error quantification, and risk mitigation of AI systems, preventing undesired outcomes and ensuring readiness for deployment.
- Different evaluation types and techniques are essential to determine the effectiveness of AI applications, especially in GenAI with its varied model outputs.
- Specific evaluation areas include correctness, relevance, safety, bias, toxicity, and task-specific metrics like accuracy and precision.
- Designing test cases with realistic inputs, expected outputs, and evaluation methods like statistical scorers, traditional ML metrics, LLM-based judgments, and code-based validations is essential.
- In a sentiment analysis system example, evaluations would assess format validation, sentiment classification accuracy, prioritization effectiveness, and final business impact to ensure system success.
- Evaluations are crucial for building reliable and valuable AI systems, regardless of complexity, ensuring they meet quality standards and deliver desired outcomes in production.
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