Abstract
While autocomplete suggestions are a feature of search engines and other applications, these are typically limited to a few words and are one-shot. Interactions with a large language model (LLM) can benefit from rich, detailed prompts since the quality of responses provided by the LLM can improve with such prompts. However, such prompts are laborious to write. This disclosure describes techniques that leverage a large language model (LLM) to automatically suggest unstructured, natural-language query completions based on LLM reasoning. With user permission, appropriate user data can be accessed and used for the specific purpose of query completion. The techniques can be implemented in an iterative manner, where autocompletion suggestions are shown in response to an initial partial query entered by the user and subsequently, upon user selection of a particular autocompletion, additional suggestions are shown to add further context or information to the query. The techniques can be used in any context where personalized autocompletion is valuable, such as a chatbot or other conversational interface, search interfaces in document management applications, etc.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
NA, "Personalized Automatic Query Expansion Suggestions", Technical Disclosure Commons, (October 29, 2025)
https://www.tdcommons.org/dpubs_series/8808