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Optimize AI products
Experiment with different models & parameters
The difference between good vs bad is in finding the right combination of parameters. Conduct head-to-head comparisons of any model or model input to improve the usefulness of your AI application. Run concurrent tests in production. Analyze user data–thumbs up or thumbs down, session activity, long-term stickiness–to inform product decisions.
Cost. Latency. Perf. Optimized.
Track model inputs, outputs, user, performance, and business metrics all in one place. Test different LLMs and parameters to optimize latency and cost. Balance quality and compute savings–does a cheaper model provide satisfactory performance in specific scenarios?
Instantly rollout new models as soon as they launch
Continuously release and iterate on AI features. Feature flagging and dynamic configs let you progressively rollout features, adjust app properties, measure the impact of their change, and rollback features without changing code.
Improve the UX with prompt experiments
Prompts have an outsized impact on a model’s output. A/B test different phrasings and contexts for prompts—should you provide a starter prompt to the user and if so what should it be? Run experiments to optimize prompts for every use case and improve user value.
Drive usage and engagement of your AI features
Test different messaging, first-time experiences, onboarding tutorials, and starter prompts to drive successful activation and reduce time to value. Run growth experiments to boost stickiness and long-term retention. Test various pricing and packaging strategies—like setting limits for free users—to optimize ROI.
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