> Source URL: /economics.guide
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title: "Prompt Engineering for Economics"
description: "How LLMs are changing economic research and financial analysis — and what you could build with them in this course."
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[@styles]: ./styles.css

# 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](https://www.alpha-sense.com), [Hebbia](https://www.hebbia.com), and Bloomberg's Document Search & Analysis let analysts query millions of filings, earnings calls, and research notes in seconds.
- [NBER](https://www.nber.org) 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](https://arxiv.org/abs/1908.10063) and [Loughran-McDonald dictionaries](https://sraf.nd.edu/loughranmcdonald-master-dictionary/).
- [Bloomberg GPT](https://www.bloomberg.com/company/press/bloomberggpt-50-billion-parameter-llm-tuned-finance/) and the open-source [FinGPT](https://github.com/AI4Finance-Foundation/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](https://elicit.com) and [Consensus](https://consensus.app) 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

[← Back to Thinking With Machines](./index.path.md)


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## Backlinks

The following sources link to this document:

- [Economics](/index.path.llm.md)