LLM Prompt Token Optimization for Accuracy, Cost, and Latency
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Solution Overview
Problem
Existing generative AI large language model (LLM) interactions face challenges in prompt quality, output quality, cost, and latency due to the length and complexity of prompts required for accurate responses, which are not efficiently addressed by current systems.
Innovation Solution
A method and system utilizing heuristic techniques and machine learning algorithms to optimize prompts by identifying areas for improvement, such as removing unnecessary words, and balancing accuracy, cost, and latency through iterative prompt reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the prompt length is increased to improve output accuracy, then the response quality improves, but the computational cost and latency increase
Solution Approach 1:
The system performs preliminary analysis of the prompt using heuristic techniques and ML algorithms to identify redundant tokens before the main LLM processing occurs. This pre-processing step removes unnecessary tokens in advance, reducing the input size that the LLM must process, thereby decreasing latency while preserving the essential information needed for accurate output.
Solution Approach 2:
The system extracts and removes redundant or unnecessary tokens from the prompt while retaining the core meaningful content. By identifying and taking out superfluous tokens through heuristic analysis and ML-based evaluation, the system reduces prompt length and computational overhead without compromising the accuracy of the LLM's output.
2Measurement precision
If the prompt length is increased to improve output accuracy, then the response quality improves, but the computational cost increases
Solution Approach 1:
The system extracts and removes redundant or unnecessary tokens from the prompt while retaining the core meaningful content. By identifying and taking out superfluous tokens through heuristic analysis and ML-based evaluation, the system reduces prompt length and computational overhead without compromising the accuracy of the LLM's output.
Solution Approach 2:
The system changes the parameter of prompt length by dynamically adjusting it based on heuristic analysis and ML algorithms. By optimizing the number of tokens to the minimum necessary for accurate output, the system reduces computational cost while maintaining output quality, effectively finding the optimal parameter setting for each prompt.
3Quantity of substance
If the prompt is simplified to reduce token count, then the cost decreases, but the output quality may deteriorate
Solution Approach 1:
The system uses feedback from ML algorithms to evaluate whether token removal affects output quality. By iteratively testing prompt variations and measuring output accuracy, the system receives feedback on which tokens can be safely removed without degrading performance, allowing continuous optimization of prompt length while maintaining quality thresholds.
Solution Approach 2:
The system replaces manual prompt crafting with automated heuristic techniques and ML algorithms that objectively evaluate and optimize prompts. This substitution enables systematic identification of redundant tokens based on data-driven insights rather than intuition, ensuring that token reduction does not compromise output quality while lowering costs.
Data Source
AI summary
Performing token optimization in generative artificial intelligence (AI) large language machine learning (LLM) interactions includes receiving, by at least one processor, a prompt from a user of a computing device, and analyzing, by the at least one processor, the prompt to identify one or more areas where the prompt can be improved. The analyzing the prompt comprises using a heuristic technique and one or more machine learning (ML) algorithms to identify the one or more areas of the prompt for improvement.