Prompt Adaptation in Programmable Network Interface Devices
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Solution Overview
Problem
Conventional approaches to improving AI model performance, such as large language models, face limitations in scalability, dynamicity, and real-time augmentation, particularly in handling user feedback and contextual information, which hinders efficient prompt tuning and adaptation.
Innovation Solution
Implementing AI model prompt adaptation in programmable network interface devices (PNIDs) that host a lightweight prompt augmentation model, enabling real-time network propagation and contextual enhancement of prompts with user feedback and trending data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional software-based prompt tuning approaches are used, then flexibility in prompt modification is improved, but real-time network propagation and dynamic adaptation to user feedback are hindered
Solution Approach 1:
The patent introduces a programmable network interface device as an intermediary component between the host device and the network. This device includes a prompt augmentation model that can dynamically modify prompts in real-time before they are sent to the AI model. The intermediary device enables both flexible prompt adaptation and real-time network propagation by processing prompts at the network interface level rather than purely in software, thus resolving the contradiction between adaptability and speed.
2Productivity
If lightweight prompt augmentation models are deployed in programmable network interface devices, then real-time prompt enhancement with user feedback is achieved, but device complexity increases
Solution Approach 1:
The patent segments the prompt processing functionality by deploying a lightweight prompt augmentation model specifically in the programmable network interface device, separate from the main AI model on the host device. This segmentation allows the network interface device to handle real-time prompt enhancement with user feedback and trending data, while the host device focuses on running the main AI model. The segmentation improves productivity by enabling parallel processing and reduces the burden on the host device, thus resolving the contradiction between productivity and device complexity.
3Measurement precision
If prompts are dynamically adapted with real-time user feedback and trending data, then AI model output quality is improved, but processing time and computational overhead increase
Solution Approach 1:
The patent implements preliminary action by pre-fetching and caching trending data and user feedback in the programmable network interface device before they are needed for prompt augmentation. The device maintains a local copy of recently accessed data and can quickly retrieve and incorporate this information into prompt modifications without requiring real-time network requests. This preliminary preparation reduces the processing time for dynamic prompt adaptation while maintaining high AI model output quality, thus resolving the contradiction between measurement precision and loss of time.
Data Source
AI summary
An apparatus includes a host interface, a network interface, and programmable circuitry communicably coupled to the host interface and the network interface, the programmable circuitry comprising one or more processors are to implement network interface functionality and are to receive a prompt directed to an artificial intelligence (AI) model hosted by a host device communicably coupled to the host interface, apply a prompt tuning model to the prompt to generate an initial augmented prompt, compare the initial augmented prompt for a match with stored data of a prompt augmentation tracking table comprising real-time datacenter trend data and cross-network historical augmentation data from programmable network interface devices in a datacenter hosting the apparatus, generate, in response to identification of the match with the stored data, a final augmented prompt based on the match, and transmit the final augmented prompt to the AI model.