An NBER working paper by Andreas Ferrara provides practical guidance on integrating large language models and generative AI into economic history research. The guide addresses a gap in scholarship by offering economists and historians concrete methods for leveraging AI tools in their field, potentially changing how historical economic research is conducted.
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Andreas Ferrara published an NBER Working Paper (No. 35374) titled "A Practitioner's Guide to Using Large Language Models and Generative AI in Economic History," offering a handbook for applying AI tools to historical economic research.
Why it matters
Economists and history researchers now have a structured resource for incorporating large language models and generative AI into their work, potentially streamlining literature review, data analysis, and historical interpretation tasks that traditionally require substantial manual effort.
What to watch
The paper is available through the National Bureau of Economic Research at https://doi.org/10.3386/w35374 for researchers interested in methodologies for AI-assisted economic history.
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