Modular LLM Orchestration for Accurate Code Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in efficiently generating accurate programming code due to the complexity of prompts required for Large Language Models (LLMs) and the inefficiencies in deploying orchestration agents, leading to higher error rates, resource usage, and increased processing latency.
Innovation Solution
A novel orchestration agent deployment that invokes specialized agents and modularized logic modules, including a programming code generation sub-LLM, to automate the generation of programming code in specified languages, reducing resource usage and improving accuracy through modularization and reusability of logic modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a complex programming language is used to form logic for automated KPI monitoring, then the automation capability is improved, but the device complexity and difficulty of operation increase
Solution Approach 1:
The system segments the complex monitoring logic into modular logic modules that can be independently developed, deployed, and maintained. Each logic module handles a specific KPI or monitoring function, reducing the complexity of the overall system while maintaining automation capability.
Solution Approach 2:
An intermediary layer is introduced between the complex programming language and the KPI monitoring logic. This intermediary provides abstractions and simplified interfaces that reduce operational difficulty while preserving the power of automated monitoring.
2Productivity
If LLMs are used to generate programming code, then productivity is improved, but error rates increase due to prompt complexity
Solution Approach 1:
The system performs preliminary actions by pre-configuring logic modules with validated logic and pre-processing prompts before code generation. This ensures that the LLM receives well-structured, context-rich prompts that reduce errors while maintaining high productivity.
Solution Approach 2:
Feedback mechanisms are implemented to validate and verify the code generated by LLMs. The system checks generated code against expected outputs and logic module specifications, correcting errors automatically and improving reliability without sacrificing generation speed.
3Ease of manufacture
If logic modules are deployed without modularization, then ease of deployment is improved, but resource usage increases and reusability decreases
Solution Approach 1:
Logic modules are segmented into independent, reusable units that can be deployed individually or in combinations. This modularization reduces resource usage by avoiding redundancy and enables efficient resource allocation while maintaining ease of deployment through standardized interfaces.
Solution Approach 2:
The logic modules are designed with universal interfaces and standardized structures that enable them to be reused across different KPIs and monitoring scenarios. This multi-functionality reduces overall resource usage while simplifying deployment through a common framework.
Data Source
AI summary
Some implementations of the disclosure provide a computer-implemented method including operations of receiving, by an orchestration agent, user input corresponding to a user question, wherein generating a response to the user question includes one of generating, editing, or refining programming code, and generating, by the orchestration agent, a prompt instructing a first sub-large language model (LLM) to perform a first task. In response to the prompt, generating, by the first sub-LLM, instructions for a second sub-LLM to perform a second task, wherein results of performing the second task by the second sub-LLM are provided to the first sub-LLM and performing, by the first sub-LLM, the first task utilizing the results of the second task generated by the second sub-LLM. An additional operation includes generating, by the orchestration agent, a GUI that displays the response to the user question, wherein the response includes or is based on the programming code.