Practical prompting, backed by research

Ask a large language model (LLM) to "find contradictions in this instruction" and you get a pile of false alarms. In the experiment, the naive variants found zero real conflicts. VeriSpec lets you check a long system prompt or rulebook for items that can't be satisfied at the same time. A report is accepted only if the model shows a concrete witness situation where both rules fire and proves that the exceptions don't resolve the conflict. Each rule is rewritten into a table along with its context (exceptions, examples from other sections) and checked in small groups. The result: precision went from 0–12% to 38.5%, and each confirmed finding became 1.5–5x cheaper. The catch: the model sees "always A" and "never A" and happily cries conflict. It doesn't check whether another section says "except in case B."

The paradox: the agents answer correctly while their shared summary quietly rots. The question simply didn't touch the false fact, so it stayed put. It'll go off on the next task. The ReGround method lets you purge falsehoods from the shared context before anyone starts relying on it. You need it when several chats, agents or people have brought in data and some of it is wrong. The trick: in the first step, hide the shared summary from the model, because it may be corrupted itself. First the model looks for an unreliable source via contradictions between reports. Then it slices the summary into individual facts and checks each one. The answer is built only on what survives.

The agent answers politely and remembers the whole context, but it's no longer itself. Ask "who are you?" and it says: "I'm Claude, and Max is just the bot's name." The method lets you catch this identity reversion in a persistent agent on Slack, Discord or WhatsApp before it starts acting weird in front of users. The persona lives in the system prompt, not in the chat history. Drop the prompt from a turn, and the history becomes a fact sheet about Max, not an order to be him. As long as the prompt is sent on every turn, there wasn't a single failure across dozens of checks.

Swap "option A" and "option B" but keep the same images. For one of the models tested, up to 60.9% of verdicts flip. Repeating the request in the same order changes only 1–10%. The method from the EditJudgeBias audit lets you tell whether you can trust an AI judge when it compares two photo-edit results. The trick: run the comparison in two mirrored orders and accept a winner only if both runs agree. That turns position bias (a pull toward the first or second slot) into a visible discrepancy. A sticker covering less than 4% of the frame moves the score more than the brightness or saturation of the whole image.