Refining the Logic of an AI Economist
In my first post, I described RoboMacro as being like a fresh graduate—full of potential but prone to making “silly mistakes.” I knew it would learn fast, but the first few weeks of running in “YOLO mode” highlighted a specific type of error that was more fundamental than just getting a headline wrong. The AI wasn’t just mistaken; it was, at times, completely backwards.
For instance, it would see a weak labor report and call it “hawkish,” suggesting it would lead to fewer rate cuts. Or it would see a surge in energy prices and argue this supported faster cuts to “mitigate the impact.” For anyone in finance, that’s not just wrong, it’s nonsensical. Weak data signals a slowing economy that needs more support (dovish), while energy spikes are inflationary and force central banks to be more cautious (hawkish).
It was a classic case of a powerful tool not having the right foundational framework. The AI could write fluently and pull in data correctly, but it lacked the grounding in first principles that a human economist internalizes over years of practice.
To fix this, we went back to the drawing board, not with the code, but with the “vibe.” We engineered a new, explicit guidance system for the writer AI. The centerpiece is a section I call the “DATA-TO-POLICY MAPPING RULES.”
It’s a simple, unambiguous framework that forces the AI to follow the correct logical path:
- Weak Data (rising unemployment, poor retail sales) = DOVISH. This signals a slowing economy, justifying more or faster rate cuts.
- Strong Data (hot inflation, strong payrolls) = HAWKISH. This signals a resilient economy, justifying fewer or slower rate cuts.
We also added specific rules for tricky situations, like energy shocks, explicitly defining them as hawkish risks. Furthermore, we completely rewrote the UK guidance to be genuinely UK-centric, removing the US-centric analysis that had bled into it.
But the work didn’t stop there. To build a truly robust system, you need layers of defense. Our next step was to create a second AI—a “Chief Editor AI”—that acts as a final quality gate. Before any note is published, it runs through a ruthless four-step audit:
- Core Logic Audit: Does the analysis follow the mapping rules?
- Factual Verification Audit: Does the text (e.g., “yields rose”) exactly match the data tables?
- Internal Consistency Audit: Does the note contradict itself?
- Final Polish: Is the formatting correct?
This two-AI system—a creative writer and a ruthless editor—is the next evolution of the RoboMacro project. The goal remains the same: to automate institutional-grade insights. But we’re learning that the path there isn’t just about powerful models; it’s about embedding them with sound, unbreakable logic.
