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LLM Evaluations: from Prototype to Production
- Investing in quality measurement in machine learning products delivers significant returns and business benefits, as evaluation is crucial for improvement and identifying areas for enhancement.
- LLM evaluations are essential for iterating faster and safer, similar to testing in software engineering, to ensure a baseline level of quality in the product.
- A solid quality framework, especially vital in regulated industries like fintech and healthcare, helps in demonstrating reliability and continuous monitoring of AI and LLM systems over time.
- Consistently investing in LLM evaluations and developing a comprehensive set of questions and answers can lead to potential cost savings by replacing large, expensive LLM models with smaller, tailored ones for specific use cases.
- The article details the process of building an evaluation system for LLM products, from assessing early prototypes to implementing continuous quality monitoring in production, with a focus on high-level approaches, best practices, and specific implementation details using the Evidently open-source library.
- Best practices and approaches for evaluation and monitoring, such as gathering evaluation datasets, defining useful metrics, and assessing model quality are discussed, alongside setting up continuous quality monitoring post-launch, emphasizing observability and additional metrics to track in production.
- The evaluation process involves creating prototypes, gathering evaluation datasets manually or using historical data or synthetic data, and assessing a variety of scenarios, from happy paths to adversarial inputs.
- Different evaluation metrics and approaches are explored, including using sentiment analysis, semantic similarity, toxicity evaluation, textual statistics analysis, functional testing for validation, and LLM-as-a-judge approach for evaluation, emphasizing the importance of proper evaluation criteria and best practices.
- A step-by-step guide is provided for measuring quality in practice using the Evidently open-source library, from creating datasets with descriptors and tests to generating evaluation reports and comparing different versions to assess accuracy and correctness based on ground truth answers.
- Continuous monitoring in production, focusing on observability, capturing detailed logs, and tracing LLM operations for effective monitoring and debugging, is highlighted, including utilizing Tracely library and Evidently's online platform for storing logs and evaluation data.
- Additional metrics to track in production beyond standard ones include product usage metrics, target metrics, customer feedback analysis, manual reviews, regression testing, and technical health check metrics, with the importance of monitoring technical metrics and setting up alerts for anomalies emphasized.
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