Prompting AI for investment decisions: a short playbook
Five prompt patterns that get useful answers out of LLMs for equity research — and three patterns that produce confident garbage.
An LLM's answer is only as good as the prompt and the data it sees. These patterns are the difference between a useful research draft and a confidently wrong one.
Patterns that work
All of these share one feature: they ground the model in source data instead of asking it to recall facts.
- Constrained summarization: 'Summarize this 10-K in 10 bullets, citing the page.'
- Structured extraction: 'Extract guidance for FY revenue and EPS from this transcript.'
- Comparative tables: 'Compare these five peers on gross margin, ROIC and FCF yield.'
- Devil's advocate: 'Write the strongest bear case against this thesis using only this filing.'
- Calibration: 'Rate your confidence 1–5 and explain the weakest link.'
Patterns that produce hallucinations
Avoid open-ended fact retrieval ('What was AAPL's Q3 2024 gross margin?') without giving the model the source — it will guess. Avoid asking for predictions ('Will NVDA beat next quarter?') — you'll get a vibe, not analysis. And never ask for a recommendation without specifying the time horizon and risk tolerance.
Keep a verification habit
Every number the model produces should be traceable to a source you can open in one click. Edge Trader Terminal enforces this by feeding the model structured FMP data and labeling outputs with their data source and timestamp.
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