Practical prompting, backed by research

The paradox: the more fully an LLM explains, the worse it teaches. A perfect answer closes the gap between "what I think I know" and "how it really is," and the brain moves on. The conversation dies after 5 turns, because information without tension doesn't demand a response. CURIOBOT turns an LLM into a tutor that deliberately doesn't close the question but holds the tension until the learner figures it out on their own. Five operators (contradiction, novelty, complexity, uncertainty, neutral) change not the content of the answer but the angle of delivery: through a contradiction, an unexpected analogy or an unresolved question. No fine-tuning, pure prompting. The result: 2.4x more turns in the conversation, because the learner doesn't want to stop.

Finding: LLMs fail at multi-factor checks. Ask one to find 10 types of problems at once and it will either miss half of them or start hallucinating ones that don't exist. This is especially critical for code review and contract analysis. Mixture-of-Prompts lets you check complex documents for dozens of problem types without hallucinations or misses. The core of the method: two stages instead of one. First you filter — determine which problem types are even possible in the data (routing). Then you activate only the relevant checks via simple propositions (P1: yes/no, P2: yes/no), which you combine with logic: problem if P1 AND NOT P2. The model answers simple questions; you control the complex logic.

Best-of-N picks a winner and throws the rest away. The problem: it's a zero-sum game. Answer quality is polylithic: one variant is great at financial analysis, another at marketing, a third at risks. Picking one = losing the rest. FusioN lets you merge the strengths of all N variants into one final answer. The method uses a fusor model as a judge-synthesizer: it sees all the variants, pulls out the best fragments of each and writes a new text. The result can beat even the Oracle (the best answer in the original pool).

Finding: by default an LLM looks for the “average” answer, the one most people would be fine with. That's exactly why its advice comes out vague: it fits everyone and no one. ALIGN lets you give the model a specific worldview, from a cautious lawyer to a thrill-seeking venture capitalist, with no fine-tuning and no extra setup. Add a paragraph with a role and core values at the start of the prompt and the model stops hunting for a “universal” answer. Instead it looks for the best answer within your system of priorities.