Abstract
In long conversations, a chatbot powered by a large language model (LLM) may start to exhibit inaccurate responses or hallucinations due to context saturation. To continue the conversation, the user needs to identify the point of likely errors and initiate a separate conversation that includes the appropriate context by copy-pasting content or other manual actions. This disclosure presents techniques for context restoration with message indexing to address this problem. Per techniques described herein, conversation turns between a user and an AI model accessed via a chatbot interface are indexed and message indexes are displayed to the user. If the output exhibits degradation that cannot be fixed via subsequent interaction, the user can provide a command to restore the conversation to any previous error-free message and fork a new branch. This causes the model context to be reset to the index of the user selected message. The techniques eliminate tedious manual inputs to reset model context and enable chatbot interactions to support a non-linear workspace where branches and forks are supported. Such a workspace can support rapid testing of different approaches without the fatigue of manual context management.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Phoughat, Aakash, "User-driven AI Model Context Restoration Based on Message Indexing", Technical Disclosure Commons, (February 02, 2026)
https://www.tdcommons.org/dpubs_series/9263