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Author: sudaangi
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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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The real cost breakdown of running LLM apps on AWS
The part of your LLM bill you do not see in the demo The first time most teams see their real LLM bill is not a happy day. The token…
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AI Observability: Stop Guessing, Start Instrumenting
The uncomfortable truth: you are flying blind Most AI incidents are not outages. They are quiet quality regressions, silent cost blowups, and vendor drift that no one notices for weeks….
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Build vs Buy in AI: A Real Decision Framework That Holds Up in Production
The honest problem Most AI teams waste quarters arguing about build vs buy, then end up doing both in the worst way: they buy a black-box API and still build…
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Hybrid search vs vector search: what actually works in production
The painful pattern The vector-only demo looks great in a sandbox. Then you ship and support tickets pile up. Acronyms don’t resolve, filters don’t filter, legal asks for deterministic behavior,…
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Why Most Enterprise AI Pilots Fail: How to Run One That Survives Production
The uncomfortable pattern The demo looks great. A slick chatbot on sanitized data, a confident deck, a six-week timeline. Then it hits the real environment: SSO, DLP rules, proxy weirdness,…
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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 RAG pipeline is slow and expensive
Your RAG is slow because it moves too much data, hops across too many services, and pays LLMs to read junk. It is expensive for the same reasons. I see…
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How to Build Real Feedback Loops Into AI Systems
The quiet failure of AI systems without feedback Most teams ship an LLM feature, celebrate a bump in usage, then stall. Quality plateaus, costs creep up, complaints trickle in, and…
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