The recent $500k loss suffered by a Polymarket trader on the MicroStrategy Bitcoin sale market exposed a critical gap in decentralized finance: the lack of automated rule auditing. In response, developer Prashant Thakur built Fine Print, a tool that cross-references a market’s current rules against historical dispute data before a trade is executed. The project was developed for the Total Recall hackathon and aims to prevent users from buying into markets where ambiguous wording has previously led to contentious resolutions.

Architecture and Data Sources

Fine Print leverages Walrus Memory on the Sui mainnet to store precedent data, utilizing three distinct account types for precedents, court rulings, and user rule versions. The system ingests live data from Tatum’s Prediction Market API for price history and uses Polygon RPC to monitor on-chain bulletin boards for real-time rule clarifications. This infrastructure allows the tool to detect when a market’s rules are updated mid-trading, a scenario that caused the price of the MicroStrategy market to collapse from 53 cents to 0.6 cents in just two minutes.

Performance and Limitations

Despite the sophisticated architecture, the tool’s performance metrics reveal significant challenges. In tests across 200 markets, Fine Print achieved an accuracy of 0.492, barely better than a coin flip, because semantic search often retrieved irrelevant precedents based on topic similarity rather than risk profile. For example, it might cite an NBA game dispute for a crypto market simply because the wording templates were similar. However, in specific high-risk scenarios involving deadline ambiguities, the tool improved accuracy from 0.417 to 0.667, suggesting that targeted pattern matching is more effective than broad semantic recall.

Key Takeaways

  • Fine Print uses Walrus Memory on Sui to store immutable records of past market disputes and rulings.
  • The tool integrates with Tatum APIs to fetch real-time price data and on-chain rule changes from Polygon.
  • Current semantic search methods struggle with precision, often citing irrelevant markets that share similar wording templates.
  • A prompt injection test confirmed the system’s security, as the judge model ignored malicious instructions embedded in market descriptions.

The Bottom Line

Fine Print demonstrates that while storing case law on-chain is feasible, the real challenge lies in matching the right risk patterns, not just similar topics. Builders need to move beyond generic semantic search and implement metadata-driven filtering to make this kind of automated due diligence actually useful for traders.