Prompt Engineering for Economics
Modern LLMs can read filings, summarize literature, transform unstructured data, and stress-test arguments faster than any research assistant. Economics, Finance-Economics, and Mathematics-Economics work is being rebuilt around them.
Where this is showing up in Economics
- AlphaSense, Hebbia, and Bloomberg's Document Search & Analysis let analysts query millions of filings, earnings calls, and research notes in seconds.
- NBER working papers and top finance/accounting journals increasingly use LLMs to extract sentiment and content from 10-Ks, Fed minutes, and earnings transcripts — building on earlier work like FinBERT and Loughran-McDonald dictionaries.
- Bloomberg GPT and the open-source FinGPT project are building finance-domain models; the Fed, BIS, and IMF have released working papers on LLMs for forecasting and policy analysis.
- Research tools like Elicit and Consensus are reshaping literature review for empirical economics and policy work.
Projects you could build in this course
- A tool that extracts and compares key metrics across hundreds of 10-K filings
- A RAG assistant trained on Federal Reserve publications or IMF reports
- A literature-review agent that synthesizes findings across econ papers on a topic