LLM-Driven Design Verification for Multi-Specialty Compliance
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Solution Overview
Problem
The conventional methods for generating technical plans for well systems in the oil and gas industry are inefficient, leading to errors and delays due to the need for iterative corrections across multiple specialties, lack of comprehensive review, and unawareness of regulatory changes, resulting in time and cost overruns.
Innovation Solution
A large language model (LLM) is trained to generate a compliance report that identifies errors in technical plans and provides corrective actions, considering all relevant legal and technical constraints by accessing a compliance database, thereby providing simultaneous compliance with multiple requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional iterative review methods are used across multiple specialties, then comprehensive error detection is achieved, but time consumption and project duration increase significantly
Solution Approach 1:
The system performs preliminary compliance checking by training the LLM on regulatory requirements and compliance constraints before the actual technical plan review. This preliminary preparation enables the model to efficiently identify errors during the review process without requiring multiple iterative passes, thus maintaining comprehensive error detection while reducing overall review time
Solution Approach 2:
A compliance database serves as an intermediary between regulatory requirements and the technical plan review process. The LLM queries this database to obtain compliance constraints and compare them against the technical plan, enabling systematic and comprehensive error detection across multiple specialties without manual iterative review
2Reliability
If multiple specialists review the technical plan sequentially, then all compliance constraints are checked, but the process becomes complex and coordination difficult
Solution Approach 1:
The system merges the functions of multiple specialists into a single LLM that has been trained on diverse compliance constraints across legal, technical, and regulatory domains. The model processes all compliance requirements simultaneously through unified prompt engineering, eliminating the need for sequential specialist reviews and reducing process complexity while maintaining comprehensive compliance verification
Solution Approach 2:
The LLM is designed as a universal review system that can handle multiple types of compliance constraints (legal requirements, technical standards, safety regulations) within a single platform. By making the system multi-functional, it replaces multiple specialized review processes with one versatile tool that maintains comprehensive coverage across all compliance areas
3Adaptability or versatility
If manual review processes are used, then flexibility in handling complex technical plans is maintained, but productivity and error correction speed decrease
Solution Approach 1:
The system changes the operational parameters of the review process by using prompt engineering to dynamically adjust how the LLM analyzes different types of technical plans. By modifying prompt structures and compliance constraint configurations, the system maintains flexibility in handling complex, varied technical plans while achieving rapid automated review and error identification, thus improving productivity without sacrificing adaptability
Data Source
AI summary
A method for generating a compliance report that includes receiving, by a report generator, a technical plan and a compliance constraint, processing the technical plan and the compliance constraint, generating the compliance report based on the processing, and providing the compliance report to a user.