I need practical guidance on harnessing Langfuse to surface both performance and user-experience problems in my production chatbot. At the moment the bot is suffering from slow response times, frequent downtime, and a worrying number of incorrect answers. On the UX side, users complain that the interface feels complex, the instructions are vague, and the overall conversation flow is choppy. Here is what I want to walk away with: • A step-by-step setup of Langfuse in my existing stack, including the correct SDK hooks for request/response tracing. • Custom dashboards or queries that clearly highlight latency outliers, error spikes, and mis-matched intents so I can pinpoint slow responses, incorrect replies, and periods of downtime. • User-journey metrics that visualise where the flow breaks down, making it easy to spot confusing instructions or loops in conversation. • A brief knowledge-transfer session (recorded or documented) so I can keep iterating without outside help. If you have hands-on experience deploying Langfuse for LLM observability—especially around OpenAI-based chatbots—let’s do this.