LLM Task Automation With Code Validation in Structured Environments
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Solution Overview
Problem
Existing artificial intelligence tools struggle to autonomously navigate and integrate into structured software environments without extensive manual guidance, hindering their ability to perform tasks seamlessly and efficiently.
Innovation Solution
A system that utilizes a large language model (LLM) to generate computer-readable instructions based on natural language inputs, which are then validated and executed to perform tasks within a controlled environment, with an AI assistant validating and correcting errors in the generated code to ensure accurate task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing AI tools are used to automate tasks, then task automation capability is improved, but the ability to autonomously navigate structured software environments deteriorates due to lack of inherent capacity without extensive manual guidance
Solution Approach 1:
The patent introduces an intermediary component that translates natural language instructions into structured code that AI tools can execute. This intermediary layer enables autonomous navigation by bridging the gap between human-like commands and machine-executable instructions, allowing AI tools to operate independently in structured environments without requiring manual guidance for each step.
Solution Approach 2:
The system implements self-service capabilities by enabling AI tools to automatically generate, validate, and execute their own operational sequences. The AI assistant autonomously navigates the software environment by generating appropriate code instructions, validating them against the environment's structure, and executing tasks without external intervention, thus improving both automation extent and autonomous operation.
2Ease of operation
If extensive manual guidance is provided to AI tools, then autonomous navigation capability is improved, but productivity deteriorates due to the time and effort required for manual guidance
Solution Approach 1:
The system performs preliminary action by pre-validating code instructions against the software environment's structure before execution. This validation step ensures that generated code conforms to the environment's requirements, eliminating the need for extensive manual guidance during task execution and thereby maintaining high productivity while achieving autonomous navigation.
Solution Approach 2:
The patent implements feedback mechanisms where the system validates generated code against the environment structure and uses this validation information to refine future code generation. This feedback loop enables the AI assistant to learn from validation results, improving its autonomous navigation capability over time without requiring manual intervention, thus maintaining productivity.
3Reliability
If validated LLM code generation is implemented, then reliability of task execution is improved, but device complexity increases due to validation and correction mechanisms
Solution Approach 1:
The patent segments the code generation and validation process into distinct modular components. The validation mechanism is separated as an independent module that checks generated code against environment specifications, and the correction mechanism is implemented as a separate feedback loop. This segmentation improves reliability through systematic validation while managing complexity by organizing functions into discrete, maintainable units.
Data Source
AI summary
A system provides an artificial intelligence assistant to automate tasks within a structured environment. The system receives a natural language instruction to perform a task within an environment that is communicatively coupled to the system. Based on the received instruction, the system generates a computer-readable input that includes a context of the environment and a computer-readable form of the received instruction. The system sends the computer-readable input to a large language model (LLM) to cause the LLM to generate a set of computer program code to perform the task. The system executes the computer program code to perform the task in the environment.