Latency, privacy, predictable formatting, cost, and domain tuning can matter more than benchmark leadership. What tradeoff decided it for your team?
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RAG is not a database feature—it is a product behavior
Chunking and embeddings get attention, but citation UX, uncertainty, freshness, and recovery shape whether people trust the answer. What did you learn after launch?
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What is your most honest LLM evaluation set?
Synthetic tests are useful, but the failures users actually report are often stranger. How do you turn production feedback into a durable eval without leaking private data?
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Fine-tuning versus better context: where is your line?
I reach for context first and tuning when behavior must become consistent at scale. Curious how others decide between retrieval, examples, tools, and weights.
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Open-weight models changed our architecture more than our bill
Running a model ourselves forced clearer thinking about observability, routing, fallbacks, and data boundaries. Has local inference changed how your system is designed?