Enterprise Code Standardization Using LLM Prompt Libraries

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Solution Overview

Problem

The use of generative AI in enterprise software development faces challenges such as accuracy and reliability of generated code, subtle logic errors, security vulnerabilities, inefficiencies, misinterpretation of nuanced business logic, and the accumulation of technical debt due to lack of explainability and rigorous validation.

Innovation Solution

A system and method utilizing a customized prompt library to direct a large language model (LLM) to recognize patterns in legacy code and generate enterprise-standardized replacement code, with a structured lifecycle for prompt development and governance, ensuring consistency and reliability across programming languages.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If generative AI is used to generate software code, then developer productivity and time-to-market are improved, but code accuracy and reliability deteriorate due to subtle logic errors and security vulnerabilities

Engineering Contradiction:

Improvedeveloper productivityVSAvoidcode accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent introduces an intermediary validation layer between code generation and deployment. This includes automated testing frameworks, code review bots, and validation pipelines that act as mediators to filter out unreliable generated code before it reaches production, thus maintaining productivity while improving reliability

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback loops where generated code is automatically tested, validated, and reviewed. Performance metrics, bug reports, and security scans feed back into the generation process, allowing the AI to learn from errors and improve code quality over time while maintaining high productivity

Inventive Principle:

Principle #23Feedback

2Loss of time

If AI-generated code is deployed without rigorous validation, then time-to-market is reduced, but technical debt accumulates due to poor code quality and maintainability

Engineering Contradiction:

Improvetime-to-marketVSAvoidcode maintainability

Core Design Contradiction:

Loss of timeVSEase of manufacture

Solution Approach 1:

The patent applies preliminary validation actions before code deployment. Automated testing, static analysis, and security scanning are performed upfront on generated code to ensure it meets quality standards before being committed to the codebase, preventing technical debt accumulation while maintaining fast time-to-market

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes parameters such as code quality thresholds, validation strictness, and testing coverage levels. By dynamically adjusting these parameters based on project requirements and risk tolerance, the system can balance rapid deployment with code maintainability, avoiding technical debt

Inventive Principle:

Principle #35Parameter changes

3Productivity

If AI is used to translate legacy code to modern languages, then modernization cost and effort are reduced, but code standardization and enterprise compliance deteriorate

Engineering Contradiction:

Improvemodernization efficiencyVSAvoidcode standardization

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The patent applies local quality by implementing language-specific and project-specific standardization rules during AI translation. Different coding standards, conventions, and compliance requirements are applied locally to different code segments based on enterprise guidelines, ensuring both efficient modernization and precise standardization adherence

Inventive Principle:

Principle #3Local quality

4Productivity

If AI-generated code lacks explainability, then development speed is improved, but debugging and maintenance difficulty increase

Engineering Contradiction:

Improvedevelopment speedVSAvoiddebugging difficulty

Core Design Contradiction:

ProductivityVSDifficulty of detecting and measuring

Solution Approach 1:

The system introduces explanatory intermediaries between the AI generation process and the final code output. This includes automated documentation generation, code comments, and explanation traces that mediate between the black-box AI and the developer, maintaining fast development while reducing debugging difficulty through improved explainability

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12455736B1Standardizing enterprise software code through LLMs

Publication Date: 2025.10.28 MORGAN STANLEY SERVICES GROUP INC

AI summary

A computer-implemented method and system standardize code patterns within enterprise software environments. A code modernization system utilizes a Large Language Model (LLM) and a specialized prompt library. The library includes pattern recognition prompts to guide the LLM in identifying specific code patterns within selected software code, potentially using enterprise-specific context. It also includes standardized solution prompts to guide the LLM in generating replacement code conforming to predefined enterprise standards for the identified patterns. The system orchestrates communication, transmitting code and relevant prompts to the LLM and receiving identified patterns and subsequently the generated standardized replacement code. This automated approach facilitates improved code quality, consistency, maintainability, and can support code translation efforts within the enterprise.