Capture analytics-ready data from your LLM app
Stop guessing how users interact with your AI product. Start tracking interactions with a privacy-safe standard built just for LLM analytics.
Learn how to build a clean, privacy-safe analytics foundation, so you can track prompts, responses, and user actions while measuring performance, and cost with confidence.
What you’ll learn about inside the handbook
Get 13 pages of implementation-focused guidance that covers the tracking essentials for AI product analytics.
Standardizing AI product events
Learn how to define three core events for prompts, model responses, and user actions with a simple schema for AI products
Modeling AI interactions as conversations
Find out how you can use conversation IDs to enable accurate analysis of multi-turn AI interactions
Analyzing AI usage without storing PII
Discover how intent classification enables you to capture valuable user behaviors while protecting privacy
Measuring LLM performance and cost
See how to track latency, token usage, and cost to understand how performance and cost vary across features and use cases
Evaluating model performance
Learn how to capture and model feedback signals and user actions to measure AI output quality over time
Implementing AI tracking in production
Get practical guidance on rolling out tracking in production while avoiding common privacy, cost, and reliability pitfalls
Get your free handbook now
Download the free guide and start leveling up your LLM product analytics today.