Practical prompting, backed by research

Discovery: an LLM catches a planted extra item in a list 6-7 times more often than its own omission — it's blind to the gaps it creates itself. The "rule instead of a list" method lets you get a complete checklist of correct answers or test cases with no silent losses. Instead of asking it to list everything — ask it to formulate a rule for checking one candidate at a time: accuracy jumps from 0.48 to 0.99, because compiling a list and evaluating a single option are different tasks for the model, even though the underlying knowledge is the same.

The paradox: the better AI handles a task, the faster you lose the ability to handle it without AI. The Explanation Gate closes the gap between "the AI did it" and "I understand it" — right in the flow of work, with no extra courses or cramming. Before using an answer, explain it in your own words while the AI grades the depth: fall short of level 3 out of 5 and you get a leading question instead of a ready-made solution. The group that used the method showed 61.5% success versus 23.1% for those using AI without restrictions — 2.5x better.

Asking an LLM to "rate this text" is like asking a random passerby to rate your work. You'll get an opinion. Not much use. The study showed: add a codebook with criteria and examples to the prompt, and the LLM evaluates at the level of an experienced expert. The method lets you use an LLM as a consistent reviewer for any text: résumés, business emails, pitches, posts. The trick: the model doesn't guess your standards, it follows them if you've written them down. Add a rubric before the task, and the model stops guessing and starts following patterns. The result: ratings become predictable and comparable to expert ones.

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.