Natural Language Policy Generation With RAG-Based Rule Validation
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Solution Overview
Problem
Existing enterprise systems require technical expertise for custom rule configuration, leading to cumbersome and error-prone processes, with manual testing being slow and prone to human error, and lack of immediate validation causing potential rule logic errors.
Innovation Solution
Utilizing generative artificial intelligence systems with large language models to automatically generate and validate rules from natural language inputs, incorporating Retrieval-Augmented Generation (RAG) and prompt engineering to ground rule generation in external data, and AI-based simulators for comprehensive testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration methods are used, then users can deploy custom rules, but the process is cumbersome and requires significant technical expertise
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between users and the enterprise system configuration interface. This mediator translates natural language policy descriptions into system configuration parameters, eliminating the need for users to directly interact with complex technical interfaces. The AI assistant handles the complexity of configuration translation, allowing users to deploy policies through simple natural language commands without requiring technical expertise in system architecture or configuration protocols.
Solution Approach 2:
The system enables users to independently configure and deploy policies without requiring assistance from system administrators or technical support. The AI-powered interface allows end users to autonomously translate their policy requirements into system configurations, perform self-validation, and deploy rules directly. This self-service capability eliminates dependency on technical expertise and empowers users to manage their own policy deployments.
2Productivity
If scripting languages are used for customization, then custom rules can be created, but individuals with limited technical training cannot quickly deploy policies
Solution Approach 1:
The patent replaces the mechanical process of manual scripting and code-based configuration with an AI-driven natural language processing system. Instead of requiring users to write, edit, and debug scripting language code, the system uses AI to automatically generate configuration scripts from natural language policy descriptions. This substitution eliminates the need for technical training in programming while maintaining the ability to create and deploy custom rules efficiently.
Solution Approach 2:
The AI assistant serves multiple functions: it acts as a natural language interface, a configuration generator, a validation engine, and a deployment manager. This multi-functional tool consolidates what previously required separate technical skills (writing scripts, validating syntax, understanding system architecture) into a single unified interface that works with any user regardless of technical background, thereby increasing productivity without requiring technical training.
3Reliability
If frequent communication with technical support teams occurs, then rule implementation can be assisted, but significant delays occur in rule implementation and testing
Solution Approach 1:
The system empowers users to independently validate and deploy their own policy rules without requiring external technical support intervention. The AI assistant provides built-in validation capabilities that check policy correctness, identify potential conflicts, and ensure proper configuration before deployment. This self-validation mechanism eliminates the need for back-and-forth communication with technical support teams, significantly reducing implementation delays while maintaining high reliability through automated accuracy checks.
Solution Approach 2:
The system performs validation and configuration checks before actual policy deployment. The AI assistant pre-validates policy logic, checks for conflicts with existing rules, and prepares configuration parameters in advance. This preliminary action ensures that policies are ready for immediate deployment without requiring post-submission review or iterative debugging with technical support, thereby eliminating delays while ensuring implementation accuracy.
4Measurement precision
If manual testing of rules is performed, then accuracy can be verified, but the process is slow and prone to human error
Solution Approach 1:
The patent replaces manual human testing with an automated AI-based validation engine. This engine systematically tests policy rules against predefined scenarios, edge cases, and conflict conditions without human intervention. The automated system eliminates human errors in testing while operating continuously at high speed, simultaneously improving both the precision of validation (through comprehensive test coverage) and the speed of testing (through automated execution without breaks or fatigue).
Solution Approach 2:
The automated validation engine operates continuously and simultaneously tests multiple policy rules across various scenarios. Unlike manual testing which must proceed sequentially and is subject to human limitations, the automated system can execute numerous test cases in parallel, providing continuous validation feedback. This continuous action ensures thorough accuracy verification while dramatically increasing testing throughput and speed.
5Adaptability or versatility
If complex rules grow in scale, then system functionality is enhanced, but understanding and maintaining policies becomes challenging
Solution Approach 1:
The patent segments complex policy maintenance into distinct AI-assisted functions: policy interpretation, configuration generation, validation, and impact analysis. The AI assistant breaks down large, complex policy changes into manageable components, analyzing each segment separately for correctness and compatibility. This segmentation makes maintaining complex rules easier by providing structured, step-by-step guidance rather than requiring users to understand the entire complex system at once, thereby preserving system functionality while reducing maintenance difficulty.
Solution Approach 2:
The system provides continuous feedback to users during policy maintenance. The AI assistant monitors rule complexity, detects potential maintenance issues, and provides real-time guidance on how to simplify or restructure policies. When users attempt to modify complex rules, the system feedbacks on the expected impact, identifies potential conflicts, and suggests optimizations. This feedback loop makes maintaining complex policies easier by keeping users informed and guided throughout the process, preventing errors before they occur.
Data Source
AI summary
At least one non-transitory computer readable media storing instructions that, when executed by one or more hardware processors, causes performance of operations. The operations include receiving a natural language query defining a policy for a workflow. The operations also include retrieving supplementary information relevant to at least one of the workflow and the natural language query, wherein the supplementary information includes at least one code fragment. The operations further include building an input prompt for a large language model. The input prompt includes the natural language query and the supplementary information, and the input prompt directs the large language model to generate, based at least in part on the at least one code fragment, code that is executable to implement the policy for the workflow.