LLM Software Provisioning via Domain-Specific Training
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Solution Overview
Problem
Traditional software development methods for enterprise solutions are resource-intensive and do not adequately incorporate domain-specific information, limiting the effectiveness of software solutions generated by Large Language Models (LLMs).
Innovation Solution
The method involves training and fine-tuning Large Language Models (LLMs) on domain-specific data and computational logic, using domain-specific embeddings to enhance the models' understanding and generation of software solutions tailored to specific industries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional software development methods are used for enterprise solutions, then software solutions can be produced, but the process is resource-intensive and does not adequately incorporate domain-specific information
Solution Approach 1:
The patent replaces traditional mechanical software development processes with an AI-based system that uses large language models to automatically generate software solutions. The system substitutes manual coding and design activities with automated AI-generated code, reducing human intervention while improving consistency and domain-specific accuracy through trained models.
Solution Approach 2:
The system changes the parameters of software development by training LLMs on domain-specific data and computational logic. This transforms the development process from generic to domain-specialized, improving the accuracy and relevance of generated software solutions while reducing the need for extensive manual refinement.
2Productivity
If LLMs are used to generate software artifacts, then software development speed may improve, but the solutions are generic and do not incorporate domain specific information adequately
Solution Approach 1:
The system performs preliminary action by pre-training LLMs on extensive domain-specific data and computational logic before actual software development. This advance preparation ensures that when the models generate software artifacts, they already incorporate domain-specific knowledge, eliminating the need for post-generation domain adaptation.
Solution Approach 2:
The patent introduces domain-specific training data and computational logic as intermediaries between the general-purpose LLM and the software generation task. This intermediary layer transfers domain knowledge to the model, enabling it to produce both fast and domain-accurate software solutions.
3Adaptability or versatility
If traditional multi-tier software designs are produced and maintained, then enterprise solutions can be delivered, but the process requires intricate designs spanning multiple tiers which is resource-intensive
Solution Approach 1:
The system segments the software development process into distinct phases: domain knowledge extraction, model training, code generation, and validation. This segmentation allows each phase to be optimized independently, reducing overall development time while maintaining adaptability through modular domain-specific knowledge bases.
Solution Approach 2:
The system performs preliminary domain analysis and model training before actual software development, preparing reusable domain-specific knowledge bases and trained models in advance. This preliminary work reduces the time required for subsequent development iterations while maintaining high adaptability to different enterprise requirements.
Data Source
AI summary
The present disclosure provides a method of facilitating provisioning of a software functionality. Further, the method may include receiving, using a communication device, an input data from a user device. Further, the method may include generating, using a processing device, an output data based on the input data and a first LLM. Further, the first LLM may be trained on training data corresponding to the software functionality. Further, the training data includes an input training data and an output training data. Further, the output training data may be generated by an implementation of the software functionality based on the input training data. Further, the method may include transmitting, using the communication device, the output data.