Most GenAI systems fail not because the model is wrong - but because the architecture around it was chosen without a framework.
Teams jump to agents, bolt on RAG, and layer frameworks until the system becomes slow, expensive, and impossible to debug. The real problem isn't the model. It's the architecture.
Here's what experienced engineers know: there are seven canonical architectures on a spectrum of increasing complexity. Each one solves a specific problem. Move right on that spectrum only when the current level fails.
The hard rule - if you can't name the specific problem that forced you to move up, you moved up too early.
This article maps all seven. For each one: what it is, when to use it, and what breaks if you choose it wrong.
Read the full breakdown here: https://ranjankumar.in/the-7-genai-architectures-every-ai-engineer-should-know
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