arXiv (All)
open access
Promptile: Recovering and Reusing Prompting Strategies from LLM Interaction Histories
As people use large language models (LLMs) over time, they develop their own ways of prompting to achieve desired results. These useful experiences often remain scattered and unorganized across conversations. Yet existing personalization approaches primarily capture user preferences or explicitly de
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Cross-Domain Evaluation of Prompting Techniques for Large Language Models#1389859
- The Ethics of Prompt Engineering#1387370
- Show Me Your Prompts! How Writers Feel About Sharing Prompts in Collaborative Text Editors#1083885
- Online LLM Personalization from Pairwise Preferences Across Users#1342080
- Longitudinal Human–AI Interaction: From Interaction Signatures to Behavioral Regimes The Signature-Induced Behavioral Regime (SIBR) Hypothesis#105564
Record · ID 1401650
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive