Practical prompting, backed by research

A pattern was found: LLMs produce "correct nonsense" in long analytical texts. The grammar is fine, the style is even, but inside there are hidden hallucinations and zero depth. The cause: RLHF trains the model to produce a "safe, averaged answer", which isn't enough for complex long-form text. The DeepNews Framework lets you generate deep analysis and investigations (3,000+ words) with 85% accuracy instead of 40%. The trick: load 10x more context than the length of the final text. At 30,000+ characters of input, the model physically shifts from "open-ended composition" to "fact compression" — triangulation becomes possible (checking a single claim against several sources). On top of that, adversarial constraints break statistical smoothness: short and long sentences mixed together, no explicit connectors like "therefore", jargon mixed with conversational language.