Structured prompt engineering is widely regarded as best practice when working with large language models and agents built on LLMs. Role assignment, task clarity, output constraints, word limits: every guide says the same thing.
This talk tests that assumption, and the results are unsettling.
In a controlled study across five leading LLMs and 25 historical prompts, structured "best practice" prompting consistently underperformed compared to relational, messy, conversational prompting - producing fewer facts, lower interpretive depth, and in some cases actively wrong answers presented with complete confidence. Hallucinations occurred exclusively under structured conditions. Under relational conditions: zero.
One model increased its fact output by 1,200% and produced 82,000 words of factually accurate, coherent, hallucination-free content in a single session under messy, relational background.
This talk explains why, in plain language, and what developers can do about it Monday morning.