Grok is one of the more user-friendly LLMs I've experienced for prediction markets because it can search the latest X messages in real-time and has comprehensive capabilities, often helping to analyze events.

However, sometimes it's quite absurd. Just now, it said a certain market had a huge edge, and a few minutes later, based on Monte Carlo simulations, it claimed the pricing was reasonable.

Why is using LLMs for predictions unreliable?

Lack of memory and feedback loop — LLMs don't remember what they've said before, always providing one-off answers.
Good at narrative pollution, bad at probability decomposition — influenced by market sentiment and news.
No skin in the game — if it makes a mistake, there's no cost, but our bets involve real money.

To truly make AI assist in prediction markets, the following must be met:

Edge has a clear threshold (e.g., ≥3%)
Decisions are traceable and backtestable (Decision Contract)
Has an Evolution Loop (Prediction → Verification → Correction)
Data support > model conclusions

The greatest role of AI should not be prediction itself, but filtering noise, discovering edges, and quantifying risks.

Final decision-making authority must rest with the player or within a system that has clear rules, is backtestable, and has a feedback loop.