RAG Hyperparameter Tuning Using Surrogate Model Inversion

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing Retrieval Augmented Generation (RAG) systems for generative AI models like LLMs face limitations in setting hyperparameters uniformly across queries and users, failing to optimize for user-specific feedback and context, leading to suboptimal performance and efficiency.

Innovation Solution

Implement a dynamic hyperparameter generation process that uses a surrogate machine learning model to predict optimal hyperparameters based on user queries, retrieved content, and user context, optimizing parameters like temperature, top 'p' answers, and response length in real-time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If uniform hyperparameter values are set for most queries, then system complexity is reduced and ease of operation is improved, but user satisfaction and performance optimization deteriorate

Engineering Contradiction:

Improveease of setting hyperparametersVSAvoidadaptability to user-specific feedback

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic hyperparameter adjustment by training a machine learning model to predict optimal hyperparameter values based on user feedback and query characteristics. The system transitions from static uniform hyperparameters to dynamic context-aware hyperparameters that adapt to each user and query, resolving the contradiction between ease of operation and adaptability.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes hyperparameter values based on predicted user feedback and query context. By using the trained model to generate different hyperparameter settings for different users and queries, the system achieves both ease of operation (automatic adjustment) and adaptability (customized parameters), directly addressing the technical contradiction.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If dynamic hyperparameter optimization is implemented, then user satisfaction and performance are improved, but system complexity and computational overhead increase

Engineering Contradiction:

Improveadaptability to user-specific feedbackVSAvoidcomplexity of hyperparameter generation system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies preliminary action by training the machine learning model offline using historical user feedback data before deployment. This pre-training phase captures complex patterns and relationships, allowing the model to make fast predictions during online query processing. The heavy computational work is done in advance, reducing real-time complexity while maintaining high adaptability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The trained machine learning model serves as an intermediary between the complex task of hyperparameter optimization and the simple interface of query processing. The model encapsulates the complexity of analyzing user feedback and determining optimal parameters, presenting a simplified prediction interface that reduces overall system complexity while maintaining adaptability.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If hyperparameters are optimized for each query and user, then user satisfaction and performance are improved, but processing time and computational resources increase

Engineering Contradiction:

Improveperformance optimizationVSAvoidtime for hyperparameter adjustment

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

By pre-training the machine learning model offline using historical data, the system prepares optimal hyperparameter prediction capabilities in advance. During online query processing, the pre-trained model quickly predicts hyperparameters based on user feedback and query characteristics, avoiding time-consuming optimization computations and reducing processing time while maintaining performance optimization.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses historical user feedback as training data to teach the model what hyperparameter values lead to satisfactory outcomes. This feedback mechanism allows the model to learn from past interactions and make accurate predictions without re-optimizing from scratch for each query, significantly reducing processing time while maintaining reliable performance optimization.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250384332A1System and Method for Real-Time Optimization of Retrieval Augmented Generation (RAG) Hyperparameters

Publication Date: 2025.12.18 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

A method, computer program product, and computing system for processing a query provided to a generative AI model. A content portion retrieved by a Retrieval Augmented Generation system for the query is processed. User context information associated with a user providing the query is determined. Hyperparameters are generated for processing the prompt with the generative AI model by processing the query, the content portion, and the user context information using run-time surrogate model inversion optimization.