RAG LLM Guardrails for Fast and Grounded Support Responses
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Solution Overview
Problem
Existing automated support systems rely on predefined paths and large language models that can generate false information, leading to unresolved user issues and inefficiencies in providing accurate and contextually relevant responses.
Innovation Solution
A system utilizing a retrieval augmented generation (RAG) framework with a large language model guardrail and judge system to dynamically identify user needs, retrieve relevant digital documents, and ensure high-quality responses by integrating a truth base and iterative improvement mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If predefined paths and large language models are used for automated support, then response speed is improved, but accuracy and reliability of information deteriorate due to false information generation
Solution Approach 1:
The patent introduces a retrieval-augmented generation framework where a separate retrieval system acts as an intermediary between the user query and the language model. This intermediary retrieves ground truth information from external sources before passing it to the language model, thereby preventing the model from generating false information while maintaining fast response times through efficient retrieval mechanisms.
Solution Approach 2:
The system performs preliminary retrieval of accurate information before the language model generates its response. By pre-fetching and verifying ground truth data from external sources in advance, the system ensures that the language model works with validated information, preventing hallucinations and improving reliability without sacrificing response speed.
2Reliability
If complex retrieval augmented generation frameworks are implemented, then response accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the automated support system into distinct modular components: a query processing module, a retrieval module for fetching ground truth information, a generation module using the language model, and a guardrail module for quality control. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by clearly defining interfaces and responsibilities between modules.
Solution Approach 2:
The system employs a universal framework that can handle multiple types of queries and information sources through a common retrieval-augmented generation architecture. The guardrail system serves multiple functions including fact-checking, quality assessment, and consistency verification, reducing the need for separate specialized systems and thereby managing complexity.
3Manufacturing precision
If iterative evaluation and guardrail systems are added, then response quality is improved, but processing time increases
Solution Approach 1:
The guardrail system implements partial evaluation by focusing on the most critical quality aspects such as factuality, consistency, and relevance, rather than performing exhaustive analysis on all possible dimensions. This selective approach ensures high response quality for the most important criteria while limiting the time overhead by not over-evaluating less critical aspects.
Solution Approach 2:
The system incorporates feedback mechanisms where the guardrail evaluates generated responses and provides corrective feedback when quality thresholds are not met. This feedback loop enables iterative improvement without requiring complete re-generation, as the system can make targeted adjustments based on specific quality deficiencies identified by the guardrail, thereby reducing overall processing time.
Data Source
AI summary
A method includes a computer receiving a user query. The computer generates a summary of the user query using a first large language model. The computer determines a user issue from a first database based on the summary. The computer determines digital document from a second database based on the user issue. The computer generates a prompt based on the digital document and a prompt template. The computer generates a response based on the prompt using a second large language model.