Why you need observability for RAG
There are so many different ways to make RAG work for a use case. What vector store to use? What retr...
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A very nice overview and intro to Langfuse, thanks for sharing!
Really happy to have this feature out of the door. Looking forward to merge this to the new Llamaindex observation implementation: docs.llamaindex.ai/en/stable/modul...
You can’t fix what you can’t see — but seeing isn’t enough.
Langfuse does a solid job tracking user ↔ LLM interactions, sure.
But if you’re trying to debug a semantic breakdown between retrieval and generation,
you’ll need a new kind of observability — one that catches things like:
These don’t show up as errors. They show up as “the answer looked fine, but wasn’t actually right.”
That’s why I open-sourced a semantic engine designed to trace the logic pathways inside RAG flows.
It’s not just observability — it’s diagnosis and semantic control.
Instead of watching your app fail and logging it, you can design it not to fail.
Reference:
thanks to everyone who contributed to this
questions -> dm on twitter: x.com/marcklingen