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Category: AI Architecture & System Design
Deep dives into designing scalable, production-grade AI systems — including RAG pipelines, LLM orchestration, multi-agent systems, and real-world architecture patterns. Focused on what works (and fails) in production environments.
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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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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 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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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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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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The hidden bottlenecks in multi-agent AI systems
The hidden bottlenecks in multi-agent AI systems Everyone loves the demo where a planner agent hands work to a researcher, who hands work to a critic, who hands work to…
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When RAG Makes Your AI Worse: Hard Rules From Production
The trap Half the RAG projects I’m asked to review would be simpler, cheaper, and more reliable without a vector index. Teams add retrieval because every diagram on the internet…
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Stateless vs stateful AI systems: what actually works at scale
The fastest way to blow your LLM budget The fastest way to blow your LLM budget is to keep shoving yesterday’s conversation back into the prompt on every turn. I…
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