Today, I want to share a particularly frustrating development experience that perfectly illustrates how well-intentioned engineering practices can lead to significant problems when misapplied. Our team encountered a maddening issue in our Mercury dashboard where comparison trail data was present in the backend response but consistently failed to display in the UI.
In the development of our Mercury tournament system, we've implemented a sophisticated two-tier approach to market analysis and comparison: a strongly-typed validation system paired with a heuristic scoring framework. This architecture has significantly improved our ability to efficiently identify optimal market conditions while maximizing computational resources.
As our Mercury trading system has grown more sophisticated, we've encountered an increasing number of subtle "BS" issues - data inconsistencies, semantic misrepresentations, and logical contradictions that can lead to poor decision-making. These issues are particularly challenging because they often involve data that is technically correct but semantically misleading.
A recent example highlighted this problem: volume slope (a percentage change value) was being formatted with volume suffixes (M for millions), completely changing its meaning from "439.74%" to "439.74 million" - a representation that could lead to dramatically wrong interpretations.
When evaluating approaches for bullshit detection in LLM outputs, we need clear criteria that prioritize practical value over theoretical elegance. This post outlines key criteria for evaluating different approaches.
After outlining our BS detection principles and criteria, let's identify the opportunities that are right in front of us. These are validation approaches we can implement quickly using our existing infrastructure.
Following our observations about LLM behavior in technical analysis, a critical question emerges: How do we systematically detect when an LLM is confidently wrong or producing nonsensical analysis? This post explores the tools and approaches available for this challenge.