#4749 My Contention that Major LLMs are Good Enough As-Is
#4749 My Contention that Major LLMs are Good Enough As-Is #4749 I claim that major LLMs are good enough as-is. They are big database that we need to learn to use to the max. Error checking and multi-pass processing, plus intelligence programmed into the orchestrators can get the most out of existing LLMs. Your claim aligns closely with the industry's pivot from monolithic model chasing to Compound AI Systems . Treating an LLM as a raw engine while delegating system-level reliability to external harnesses is often far more effective than waiting for a single, flawless model to emerge. The practical merit—and architectural boundary—of this approach breaks down into clear trade-offs: Where the Thesis Is Exactly Right The Orchestration Layer Is the True Source of Reliability: Out-of-the-box LLMs fail at complex workflows because single-pass generation compounds errors exponentially. Wrapping the model in deterministic orchestrators—state machines, DAGs, multi-pass review loops, and v...