> Source URL: /history.guide
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title: "Prompt Engineering for History"
description: "How LLMs are changing archival research and historical scholarship — and what you could build with them in this course."
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[@styles]: ./styles.css

# Prompt Engineering for History

Historians now have tools that can read a handwritten 18th-century ledger, translate it, and let you query it in plain English. RAG, OCR, and long-context models are reshaping archival research, teaching, and public history.

## Where this is showing up in History

- Google's **[NotebookLM](https://notebooklm.google)** (Gemini-powered RAG over uploaded primary and secondary sources, with Audio and Video Overviews) is quickly becoming a standard tool for research notebooks and seminar prep.
- **[Transkribus](https://www.transkribus.org)** (READ-COOP, evolved from the EU TranScriptorium and READ projects) does handwritten-text recognition in 100+ languages and has processed 200M+ pages, exporting to TEI-XML for digital-humanities pipelines.
- The **American Historical Association's** *[Guiding Principles for Artificial Intelligence in History Education](https://www.historians.org/resource/guiding-principles-for-artificial-intelligence-in-history-education/)* (approved July 29, 2025, published Aug 5, 2025) lays out 14 principles across historical thinking, AI literacy, and classroom policy.
- **[Library of Congress Labs](https://labs.loc.gov)** and the **[Smithsonian Open Access](https://www.si.edu/openaccess)** initiative are publishing machine-learning-ready collections and hosting experiments that apply LLMs to digitized archives.

## Projects you could build in this course

- A RAG assistant over a specific primary-source archive (e.g., Civil War letters, WPA narratives)
- A Transkribus-to-structured-data pipeline that turns handwritten records into a queryable dataset
- An agent that drafts annotated bibliographies from a corpus of secondary literature

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


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## Backlinks

The following sources link to this document:

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