Controlled LLM Code Execution for Safe Natural-Language Automation
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Solution Overview
Problem
Foundation models (FMs) require a supervised environment for code execution, limiting their accessibility and usability for non-engineers, as they need terminal interaction and manual supervision to ensure proper functioning.
Innovation Solution
A controlled Large Language Model (LLM) Code Interpreter and Execution Environment (LCIEE) that translates user requests into executable code, manages execution errors, and interacts with external APIs, providing a safe and reliable coding environment without requiring a software engineer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Foundation Models are used to generate code from natural language requests, then code generation capability is improved, but the system requires terminal interaction and manual supervision which increases operational complexity
Solution Approach 1:
The patent introduces a controlled execution environment that acts as an intermediary between the Foundation Model and the terminal/system resources. This environment manages code execution, handles terminal interactions, and provides supervision automatically, eliminating the need for manual user intervention while maintaining safety and control.
Solution Approach 2:
The system implements self-service mechanisms where the controlled execution environment automatically manages code generation, execution, error handling, and termination without requiring manual supervision. The system serves itself by autonomously completing tasks that previously required human engineer intervention.
2Reliability
If manual supervision is required to ensure code execution safety, then execution reliability is improved, but accessibility to non-engineers deteriorates
Solution Approach 1:
The controlled execution environment serves as a mediator that ensures safe code execution while presenting a simple interface to users. It handles all safety-critical operations in the background, allowing non-engineers to interact with the system through natural language without needing to understand or manage the underlying safety mechanisms.
Solution Approach 2:
The system performs self-supervision through automated safety checks, error detection, and execution control within the controlled environment. This eliminates the need for human supervision while maintaining execution safety, making the system accessible to users without engineering expertise.
3Reliability
If a controlled execution environment is implemented to manage code safety, then execution reliability is improved, but system complexity increases
Solution Approach 1:
The patent segments the system into distinct functional components: the Foundation Model for code generation, the controlled execution environment for safe execution, and the interface layer for user interaction. This segmentation allows each component to be optimized independently and managed separately, reducing overall system complexity while maintaining execution reliability.
Data Source
AI summary
Methods, systems, and computer programs are presented for implementing a tool that generates and executes the code in a controlled environment to satisfy user requests entered as text prompts. One method includes receiving a user prompt with a user request received in a user interface (UI), generating a first Large Language Model (LLM) prompt to create a plan, and receiving the plan from an LLM, the plan comprising text describing a sequence of operations. The method further includes generating a second LLM prompt, to create code, specifying which code instructions are permitted in the created code. Further, the method receives the code from the LLM that received the second LLM prompt, and executes the code in a controlled environment. Further, the results generated by executing the code are presented in the UI.