Prompt relevance testing: Systematically evaluate prompt outputs versus ground truth data or human evaluations to refine prompts for maximum usefulness.

Output utility analysis: Assess how often and in what ways real users leverage or engage with AI-generated content to focus improvements on most valuable outputs.

User feedback correlation: Analyze correlations between user satisfaction feedback on AI interactions and attributes like prompt versions, personas, and models to guide optimizations.

Conversation analytics: For conversational AI, evaluate dialog analytics like intent recognition accuracy, slots filling rates, and fallback rates to improve conversations.

Interaction analytics: Analyze user interaction patterns with AI features. Examples include queries, dwell times, clicks, conversions, and arranges. The insights identify refinement areas.

Demographic analytics: Segment key performance metrics like satisfaction, utility, and error rates by user demographics. The breakdowns reveal which user groups to focus improvements on.

Multivariate testing: Run A/B tests varying prompts, models, endpoints, and hyperparameters together to uncover which combinations optimize objectives like conversion, satisfaction, and engagement.

Data visualization: Visually explore usage analytics broken down by attributes like time, geography, user cohorts, devices, and versions. Visual inspection often exposes unexpected insights.

Application feature analytics: Track integration points between AI and other parts of the product experience to identify handoff points to optimize and areas where tighter integration would benefit users.

Cost efficiencies: Continuously analyze the costs versus value delivered by different AI feature configurations and usage levels to right size investments.

Forecasting analytics: Apply predictive analytics techniques like regression to estimate future AI costs, capacity needs, and potential business value based on historical trends.