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Productify
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How to experiment in the world of AI
- Product Managers and leaders are shifting from traditional product management to building AI-enabled or AI-first products where the same rules of experimentation are no more applicable.
- Experimentation in the emerging world of traditional AI and GenAI involves new approaches, such as involving data scientists and machine learning engineers, parallel testing and iterations based on data.
- The metrics used to evaluate experiment success differ significantly between traditional product management and AI/Generative AI (GenAI) contexts.
- AI experiments can involve multiple models or configurations of such models (on parameter level or training set level) tested simultaneously (multivariate testing).
- Customer problem always remains the key aspect to be addressed when testing AI-enabled features, and metrics like engagement, conversion rates, Customer Satisfaction Score (CSAT), retention rate, and profits per user are often evaluated.
- GenAI experiments prioritize user-centric metrics that assess the quality and relevance of generated content, such as user satisfaction scores, engagement metrics, and content relevance.
- Product managers can enhance recommendation systems by continuously monitoring and improving precision, recall, and F1 score in AI-enabled experiments.
- In GenAI experiments, data teams develop chatbots that generate content by using models, such as GPT-4, and track metrics such as user satisfaction scores, engagement metrics and content relevance.
- Traditional AI experiments rely heavily on accuracy, precision, and F1 score to evaluate model performance.
- PMs help identify potential risks associated with deploying AI models, including ethical considerations and model bias, ensuring that these issues are addressed proactively.
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