Stop Building AI Products With a Single LLM — It's a Trap

You've seen the demos. A single GPT-4 call that "does everything." Summarizes documents, writes code, answers customer queries, generates reports — all from one monolithic prompt. It looks magical in a demo. It falls apart in production. We learned this the hard way at Gerus-lab . After shipping 14+ AI-powered products across Web3, SaaS, and automation, we can tell you with absolute certainty: the single-LLM architecture is a dead end. Here's why — and what actually works. The "One Model to Rule Them All" Fallacy The pitch is seductive: just throw everything at GPT-4o or Claude and let the magic happen. But here's what actually happens when you do that in production: Context windows overflow. Your 128K tokens sound huge until you stuff system prompts, RAG results, conversation history, and tool definitions in there. Suddenly you're truncating critical data. Costs explode. Every request processes your entire mega-prompt. A simple "what's the weather?" query costs the same as a complex m