LLM Prompt Resource Usage Prediction and Display
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Solution Overview
Problem
Large language models (LLMs) face resource usage limitations that can lead to rejected prompts or poor output quality due to unawareness of resource caps, causing inefficiencies and wasted resources, as users are typically not informed about the resource usage parameters.
Innovation Solution
A method and system that compute and display resource usage parameters associated with prompts to LLMs, using a trained resource prediction model to estimate the likelihood of reaching resource limits, providing real-time feedback to users through a user interface, allowing them to adjust inputs and avoid exceeding resource thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If resource usage caps are imposed on LLM prompts, then resource management is improved, but prompt length and output quality deteriorate
Solution Approach 1:
The system provides real-time feedback to users about resource usage by displaying the current prompt length and estimated token consumption. This feedback loop allows users to adjust their prompts before submission, ensuring they stay within resource limits while maintaining output quality.
Solution Approach 2:
The system calculates and displays resource usage estimates before the prompt is actually processed by the LLM. This preliminary assessment allows users to modify their prompts in advance to avoid exceeding resource caps, preventing rejection and ensuring quality output.
2Device complexity
If users are not informed about resource usage caps, then system complexity is reduced, but resource efficiency deteriorates
Solution Approach 1:
The system automatically calculates and displays resource usage information without requiring users to manually track or estimate token consumption. This self-service approach maintains simplicity for users while improving resource efficiency through informed prompt construction.
Solution Approach 2:
By providing automatic feedback on resource usage, the system enables users to optimize their prompts without adding significant complexity. The feedback mechanism is integrated into the existing interface, maintaining ease of use while dramatically improving resource efficiency.
3Reliability
If prompt length is increased to improve output quality, then resource usage increases, but resource caps cause prompt rejection
Solution Approach 1:
The system allows users to draft prompts that may initially exceed resource limits, then provides feedback to guide them to optimize to partial action - just enough length to achieve quality output without exceeding caps. This enables iterative refinement of prompts to the optimal length.
Solution Approach 2:
Real-time feedback on token consumption and estimated resource usage allows users to adjust their prompts to achieve the maximum effective length within resource caps, optimizing both quality and resource efficiency.
4Quantity of substance
If real-time resource monitoring is implemented, then resource management is improved, but system complexity increases
Solution Approach 1:
The resource monitoring system operates automatically in the background, calculating token usage and displaying information without requiring active user management or complex configuration. This self-service approach improves resource management while maintaining system simplicity.
Solution Approach 2:
The system monitors and displays key resource parameters (token count, estimated usage) that change dynamically as users type. This parameter-based monitoring provides comprehensive resource management through simple, interpretable metrics rather than complex system changes.
Data Source
AI summary
Methods and systems for indicating a resource usage parameter for prompting a large language model (LLM) are described. A user input is received, from an electronic device, for generating a prompt to a LLM. A prompt resource usage parameter is computed based on the user input. A trained resource prediction model is used to generate a predicted response resource usage parameter for a response from the LLM, based on the user input. A total resource usage parameter is computed, based on the prompt resource usage parameter and the predicted response resource usage parameter. A representation of the total resource usage parameter is communicated to the electronic device, to cause the electronic device to provide an output of the representation of the total resource usage parameter.