Stop guessing why your LLM failed – PostHog LLM Analytics Demo

Learn how to monitor, debug, and optimize your LLM-powered features using PostHog's LLM Analytics. In this hands-on tutorial, Michael from the PostHog AI team demonstrates: ✅ Tracking LLM usage metrics: users, traces, and model costs: ✅ Monitoring unit economics for AI features in real-time ✅ Debugging LLM calls with latency and error tracking ✅ Using traces to understand how your LLM arrives at answers ✅ Inspecting prompts, tokens, and costs at every step ✅ Building resilient AI features with proper observability ✅ Integration with LangGraph and the PostHog SDK Watch a live debugging session where we add web search capabilities to PostHog AI and use traces to verify the entire LLM workflow – from input state to final output, including tool calls, prompts, and cost analysis. Perfect for AI engineers, full-stack developers, product engineers building LLM features, and anyone working with Claude, GPT, or other language models. 🔗 Try PostHog: https://posthog.com 📚 LLM Analytics Docs: https://posthog.com/docs/ai-engineering 📖 LangGraph Integration: https://posthog.com/docs/ai-engineering/langchain-integration#llm #AI #PostHog #AIEngineering #LangChain #LangGraph #MachineLearning #Anthropic #Claude What is PostHog? – PostHog is an all-in-one developer platform for building successful products. We provide product analytics, web analytics, session replay, error tracking, feature flags, experiments, surveys, LLM analytics, data warehouse, CDP, and an AI product assistant to help debug your code, ship features faster, and keep all your usage and customer data in one stack.