Better questions
for smarter tools.

Give AI a research job you can inspect

An open-ended request to “predict the outcome” hides several different tasks. A more useful workflow separates question extraction, evidence collection, competing explanations, probability estimation, and review. Each stage needs a clear input and a result someone else can check.

Start with a brief that records the exact event, time cutoff, known evidence, missing information, and reasons to update. Our AI research guide explains that workflow without treating fluent language as proof of forecasting skill.

LLMs: test the forecast, not the confidence

A forecast should be recorded before the outcome is known and scored against a defined evaluation set. Preserve the information available to the model, including the retrieval cutoff. Compare with a relevant baseline and include unresolved, missing, and unsuccessful predictions in the methodology.

Our LLM forecasting and calibration guide uses a small fictional scoring example to explain what a metric can and cannot tell you. A score from one set of questions should not be presented as a general guarantee of performance.

Bots: permissions are part of the design

A bot adds repeated software behavior to a forecast. Before any experiment, define what it may read, write, and request. Keep a paper-trading environment separate from real actions. Record assumed fills, fees, delays, and failure modes so a simulated result is not mistaken for an achievable return.

The prediction bot controls guide discusses bounded actions, stop conditions, and review. This site provides the reading material, not a trading bot or an execution service.

Agents: keep a record of the work

An agent may choose which source to inspect next, use a tool, or revise a draft. Record those steps along with the evidence and any error. The final answer should make unresolved questions visible instead of hiding them behind a polished summary.

Read agentic prediction markets for a proposed audit trail and human handoff. The structure is useful for editorial research even when the system has no permission to act on a market.

Questions to ask of any AI research result

• Is the event definition identical to the question being evaluated?
• What information was available before the stated cutoff?
• Which sources disagree, and has that disagreement been preserved?
• What baseline or scoring rule supports the performance claim?
• Which actions, if any, required a human decision?

Keep these questions close to the work. They help a reader understand what the system produced, how it was reviewed, and what remains uncertain.

Primary reference: NIST: Artificial Intelligence Risk Management Framework 1.0. Check the current documentation for product-specific details.