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Tag: AI System Design
Patterns and best practices for designing scalable and reliable AI systems.
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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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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 AI Teams Struggle Without a System Design Mindset
Most AI outages I get called into are not model problems. They are system problems wearing model symptoms. The app is slow, answers change between retries, costs spike on Tuesdays,…
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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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The biggest misconception leaders have about AI implementation
The painful truth: your AI problem is not the model If your team is stuck swapping models every month and your roadmap keeps slipping, you are likely chasing the wrong…
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More Data Won’t Fix Your AI System
The common failure mode: “let’s just add more data” I see this play out every quarter. Metrics flatten, users complain about wrong answers, latency creeps up. Someone proposes a fix…
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Caching strategies for LLM systems that actually work
The silent reason your LLM bill is 2x higher than it should be If your latency is spiky, your OpenAI or self-hosted bill is creeping up, and your team keeps…
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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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Common mistakes in AI architecture design that cost you uptime, accuracy, and money
The recurring smell Most AI outages I get called into are not model problems. They are architecture problems disguised as model issues. Latency spikes, random failures, wrong answers, costs drifting…
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