Viewing enriched Twitter post
⭐new MLOps preprint⭐ RAG is everywhere, but building RAG is still painful. When something breaks--the retriever? the LLM?--developers are left guessing, & iterating is often slow we built a better way & used it as a design probe to study expert workflows 👇 https://t.co/3kqrW9jSWr
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"full_text": "Meet raggy: an interactive debugging interface for RAG pipelines. It pairs a Python library of RAG primitives with a UI that lets devs inspect, edit, & rerun steps in real time. raggy precomputes many indexes for retrieval upfront, so you can easily swap them out when debugging! https://t.co/HZkaCIBowY",
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"text": "Then, to learn more about expert workflows, we ran a study with 12 engineers who’ve built production RAG pipelines. We simulated a question-answering application from a hospital & watched our participants use raggy to build and iterate on their pipelines. The paper reports a bunch of qualitative findings, including:\n\n🔍 They always debug retrieval first\n⚙️ Fixes to one step often break another\n⚡ Fast iteration was key: raggy turned half-day experiments into seconds!!",
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