Dynamic LLM Prompt Evaluation for Model Selection and Output Validation

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Solution Overview

Problem

Existing software development systems lack intuitive, consistent, and reliable methods for selecting appropriate large language models (LLMs) and designing prompts, leading to inefficiencies and security risks, such as resource misuse and potential data breaches.

Innovation Solution

A data generation platform that dynamically evaluates machine learning prompts for model selection and validates outputs, using authentication, performance metrics, and prompt validation models to ensure secure, efficient, and modular data pipelines.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual selection and configuration of LLMs is required, then model performance can be optimized, but system complexity and operational difficulty increase

Engineering Contradiction:

Improvemodel performanceVSAvoidoperational difficulty

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system automatically selects and configures LLMs based on prompt analysis, eliminating the need for manual configuration. The prompt validation model autonomously evaluates prompts and routes them to appropriate models, enabling self-service operation while maintaining optimal performance.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically adjusts model selection parameters based on prompt characteristics and performance metrics. By monitoring performance data and changing routing parameters automatically, the system optimizes model selection without manual intervention while adapting to varying operational conditions.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If comprehensive prompt validation is implemented, then security and reliability improve, but processing time and system complexity increase

Engineering Contradiction:

ImprovesecurityVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The prompt validation model performs preliminary evaluation of prompts before they are processed by LLMs. By validating prompts in advance and pre-classifying them into appropriate model categories, the system ensures security and optimizes routing without adding significant processing delay to the main workflow.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The prompt validation model acts as an intermediary between user inputs and LLM processing. It mediates by filtering, validating, and routing prompts, thereby protecting the system from malicious inputs while maintaining efficient processing through automated classification and routing mechanisms.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Adaptability or versatility

If multiple LLMs are deployed for different tasks, then functional versatility improves, but resource management complexity increases

Engineering Contradiction:

Improvefunctional versatilityVSAvoidresource management complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments different LLMs into distinct categories based on their specialized functions (e.g., code generation, data processing, creative writing). The prompt validation model then routes specific prompts to corresponding model segments, enabling functional versatility while simplifying resource management through organized categorization and automated routing.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The prompt validation model serves as a universal routing mechanism that handles all prompt types and directs them to appropriate specialized models. This multi-functional approach allows the system to maintain versatility across different tasks while using a single unified validation and routing infrastructure to manage resources.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Productivity

If performance monitoring and model switching is implemented, then productivity improves, but system complexity increases

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system implements performance monitoring that feeds back into the prompt validation model, enabling automatic model switching based on real-time performance data. By continuously monitoring metrics such as response time and accuracy, the system dynamically adjusts model routing to optimize productivity while managing complexity through automated feedback-driven decisions.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250322046A1Dynamic evaluation of language model prompts for model selection and output validation and methods and systems of the same

Publication Date: 2025.10.16 CITIBANK N A

AI summary

The systems and methods disclosed herein relate to a model validation platform that enables dynamic validation of a user's prompt for a large language model (LLM) in order to evaluate the validity of the prompt and the suitability of a large language model for processing the prompt. For example, the platform enables an estimation of the resource allocation associated with processing the prompt with a given LLM, as well as a modification of the prompt, prior to the processing the prompt with the selected LLM. The platform can further validate the output prior to transmitting the output to a server system for display to the user. By doing so, the platform enables dynamic evaluation of a request to execute an LLM, as well as evaluation of resulting outputs, for accuracy and efficiency improvements in data processing or software development pipelines.