I made a system that detects what you're asking about and makes the AI think like an expert in that field before answering. The difference is astonishing.

A developer has tackled a core weakness of modern LLMs: their tendency to provide confident but generic or sycophantic answers. The solution is a two-part system called 'domain methodology injection.' First, an LLM-based classifier analyzes a user's query and automatically applies deep, research-backed reasoning frameworks from one of eight expert domains, including legal, medical, and finance. These frameworks enforce the specific logic and questioning patterns a real specialist would use, blocking the AI's default path to vague, non-committal responses.

Second, the system orchestrates a multi-model debate to refine the answer. The domain-injected prompt is sent simultaneously to five different AI models: Claude, GPT, Gemini, Grok, and Mistral. These models then see each other's responses and engage in multiple rounds of debate, challenging weak reasoning. Finally, a synthesis engine analyzes the entire debate to produce a single, consensus-driven answer. The creator argues that combining expert methodology prompts with a multi-model architecture is key, as good prompts on a single model offer no verification, while multiple models with bad prompts just produce more variations of low-quality output.