Practical prompting, backed by research

Ask an LLM the probability of the same event twice and you'll get two different numbers: 55% and 75%. No logic involved, just guessing dressed up as expertise. AuditForecast lets you get a stable, checkable forecast that doesn't shift from question to question and that can be challenged step by step. The method separates the base rate from individual news-based adjustments — each adjustment gets its own multiplier, and at the end all the odds are simply multiplied together.

The model is wrong with the same confidence on real and faked data: the numbers don't matter, only the packaging does. Without context, an AI agent honestly says "this is unpredictable" 9 times out of 10. But show it a table with RSI and MACD, and confident forecasts shoot up almost 6-fold, even if every number on the dashboard was invented from scratch. The method separates in time the moment of judgment from the moment the data is shown — first we ask the model whether the question is knowable, and only then show the "evidence."

Finding: LLMs are catastrophically bad at holding implicit structure in problems with formulas. They calculate correctly but output 6.3% instead of 0.063. They confuse "independent of the variable" with "equal to zero". They wander blind even when they can do the arithmetic. The F-1 method lets you solve applied problems with formulas more accurately: financial calculations, physics, business metrics. It works in two steps inside one prompt: first the model explicitly formalizes the problem (givens → find → equations), then picks its own strategy: direct calculation, step-by-step reasoning (CoT) or code (PoT). The structure of the equations dictates how to solve. +13.3% on finance, +2.55% on physics over plain CoT.

The model generates token by token, left to right. If it starts with "Great idea, put your whole pension into crypto...", it's already too late: it's on that path and can't turn back. InvThink fixes this with inverse thinking: the model first lists ways to cause harm, then analyzes the consequences, then formulates constraints, and only then generates the final answer. In high-stakes decisions (medicine, finance, law) the method cuts harmfulness by 30.4%, and in scenarios where an AI agent faces a goal conflict it completely eliminates harmful actions: a 0.00 harmful rate.