Template-Based Tuning for Enterprise Generative AI Accuracy
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Solution Overview
Problem
Existing generative machine learning models struggle to provide comprehensive and accurate answers within enterprise systems due to a lack of knowledge about enterprise data, outdated information, and the inability to ensure responses are based on user access permissions, leading to fragmented user experiences and reduced productivity.
Innovation Solution
A natural language generative application service that integrates template-based tuning with retrieval augmented generation, using a shared template to fine-tune models with enterprise data, ensuring responses are relevant and secure by leveraging data retrieval and access control, and providing human-like citations for verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing generative machine learning models are used to answer enterprise questions, then they can provide general knowledge responses, but they lack knowledge about enterprise data and provide outdated or inaccurate information
Solution Approach 1:
The patent introduces a retrieval-augmented generation system that acts as an intermediary between the generative model and enterprise data sources. The retrieval component fetches current enterprise information (documentation, code repositories, issue trackers) and provides it to the generative model, enabling accurate responses based on up-to-date enterprise data while maintaining the model's natural language generation capabilities
2Adaptability or versatility
If generative models provide unrestricted access to enterprise data, then they can comprehensive answers, but they cannot ensure responses are based on user access permissions
Solution Approach 1:
The patent implements local quality by applying different access control rules to different parts of the enterprise data ecosystem. The system determines user permissions specific to each data source (documentation, code repositories, issue trackers) and retrieves only the portions of enterprise data that the user is authorized to access, ensuring comprehensive yet permission-compliant responses
3Loss of information
If multiple different systems or services are checked to locate desired information, then complete information can be obtained, but the process becomes fragmented and reduces productivity
Solution Approach 1:
The patent merges multiple enterprise data sources (documentation systems, code repositories, issue trackers, and other services) into a unified retrieval-augmented generation system. The model can query all these systems simultaneously through a single interface, automatically synthesizing information from diverse sources without requiring users to manually check each system, thereby maintaining information completeness while dramatically improving productivity
4Reliability
If generative models are fine-tuned with enterprise data using template-based tuning, then they provide accurate enterprise-specific answers, but the model tuning process becomes complex
Solution Approach 1:
The patent segments the enterprise data into structured templates with specific fields (titles, descriptions, code snippets, error messages, stack traces). This segmentation allows the retrieval-augmented generation system to efficiently query and retrieve relevant information from each template type, reducing the complexity of fine-tuning by providing organized, queryable data structures that the model can work with more easily
Data Source
AI summary
Template-based tuning is performed on a generative machine learning model where a shared template is used to tune the generative machine learning model across multiple natural language tasks. When a natural language request to perform a natural language task is received, portions of a shared template to complete are identified as part of generating a prompt. The generative machine learning model is instructed according to the generated prompt and a response to the request is returned based on a result of the generative machine learning model.