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Abstract

This disclosure presents an artificial intelligence-based system that leverages Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to contextually educate, assist, and recommend printer features to users. End-users of printing devices frequently underutilize available product features due to fragmented and inaccessible product information. Feature documentation is dispersed across datasheets, embedded help systems, and release notes, that are often verbose, overly technical, or not contextually surfaced at the point of need, resulting in low feature discoverability and suboptimal device utilization. This approach improves feature adoption, accelerates issue resolution, and enhances the overall user experience with the printing device.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.

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