Multi-Stage LLM Prompt Processing for Context-Aware Commands
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Solution Overview
Problem
Conventional methods for generating commands for large language models (LLMs) often result in incorrect or incomplete outputs due to the complexity of natural language and variability in user inputs, particularly in complex scenarios requiring precise and context-aware responses.
Innovation Solution
A cascading approach using multiple specialized instances of LLMs, each trained and prompted differently, to process complex prompts, where outputs from one instance are fed into subsequent instances to ensure accurate and complete command generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single LLM is used to process complex prompts, then the system structure remains simple, but the accuracy and completeness of command generation deteriorates
Solution Approach 1:
The patent divides a complex prompt processing task into multiple sequential stages, each handled by a specialized LLM instance. The first LLM processes the initial prompt to generate intermediate results, which are then passed to a second LLM for further processing and refinement. This segmentation allows each model to focus on specific aspects of the task, improving overall accuracy while maintaining manageable system complexity through modular architecture.
2Measurement precision
If conventional prompting techniques are used, then the system is easy to operate, but the precision and context-awareness of responses deteriorates
Solution Approach 1:
The patent introduces an intermediary processing layer between the user prompt and the final response. The first LLM acts as an intermediary that transforms the original prompt into refined intermediate representations, which are then processed by the second LLM. This intermediary approach enhances precision by adding contextual understanding and logical reasoning steps, while the automated cascading architecture maintains ease of operation through seamless multi-model coordination.
3Reliability
If multiple specialized LLM instances are used in cascade, then the accuracy and completeness of commands improves, but the computational resources and processing time increases
Solution Approach 1:
The patent employs preliminary action by having the first LLM perform initial processing and generate intermediate results before passing them to the second LLM. This staged approach allows each model to prepare and refine information progressively, ensuring completeness of command generation. The automated cascading architecture optimizes processing time by eliminating redundant operations and enabling parallel processing where applicable, balancing thoroughness with efficiency.
Data Source
AI summary
Methods and descriptions are described herein for applying cascading machine learning models to command prompts. In particular, the system may receive a query indicating a computing process to be performed. The system may input a command prompt based on the query into a first instance of an LLM, which may output activities for performing the process. The system may input a first activity into a second instance of the LLM, which may output vulnerabilities associated with the first activity. The system may input a first vulnerability into a third instance of the LLM, which may output indications of available control tools for addressing the first vulnerability. The system may input a first control tool into a fourth instance of the LLM, which may output indications of monitoring tools for monitoring the first control tool. The system may then cause implementation of the first control tool and the first monitoring tool.