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Category: Generative AI in Production
Practical insights on taking GenAI applications from PoC to production — covering latency, cost, reliability, and deployment challenges faced by real companies.
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Why Most RAG Architectures Break Under Real User Load
The demo worked. The production launch didn’t. The pattern is predictable. The RAG demo looks great in a room with five people. Then you hit 200 to 800 QPS and…
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Designing the accuracy-latency trade-off in production AI
Your offline eval says 92% accuracy. Your users bail at the spinner. I have seen a 30% drop in chat engagement when time-to-first-token drifted from 500 ms to 1.8 s,…
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Why your LLM response time is inconsistent
The real reason your LLM is fast at 11 am and painful at 3 pm You ship a chat feature. Median comes back in 800 ms in staging. In prod,…
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Scaling GenAI from PoC to Production: What Breaks and How to Fix It
The uncomfortable gap between a great demo and a stable product The PoC nails a few curated prompts. The team celebrates. Two weeks later the first production users show up…
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The AI Demo Trap: Closing the gap to real business value
The painful pattern A team ships a slick internal demo. It answers questions, writes code, summarizes PDFs. The room nods. Then you wire it to real data, real users, real…
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Streaming vs batching in LLM systems: how I decide in production
The painful truth about streaming vs batching If your chat UI feels snappy in the demo but falls apart under real traffic, you probably picked the wrong side in the…
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Designing low latency AI for real time: what actually works
The real problem with “real time” AI Your p50 looks fine. Your users don’t care. They feel the p95. I’ve walked into teams with a neat demo, then watched the…
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LLM Latency In Production: What Actually Works
The spinner is lying to you If your LLM app shows a typing effect in under 300 ms but p95 completes at 6 to 10 seconds, users feel the lag….
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