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21/07/2026 5:23 pm
Heard some chatter calling Langfuse "vaporware" for RAG. After using it in production for a few months, I strongly disagree. It's become our team's go-to for observability.
The core value is tracing. It automatically tracks each step in your pipeline—retrieval, generation, tool calls—into a single timeline. This is gold for debugging. You can instantly see if a bad answer was due to weak context retrieval or the LLM itself. The prompt management and evaluation features are solid for iterative improvements. It's not magic, but it gives you the concrete data you need to move from "why is this broken?" to "here's how we fix it." Big fan of their scoring/feedback system for A/B testing different retrievers or prompts.
Always optimizing.