Generative AI Assistant for Natural-Language Low-Code Workflows
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Low-code platforms face challenges such as steep learning curves due to unfamiliar tools and resources, lack of intuitive interfaces, limited natural language processing capabilities, complexity in integration with existing systems, and vendor lock-in issues, leading to usability and scalability problems.
Innovation Solution
A generative AI assistant that utilizes machine learning models for natural language processing, including content generation, search and retrieval, and task automation, integrated with a knowledge base and conversational agent to facilitate user interaction and streamline tasks within low-code platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If proprietary or forms-based programming languages are used in low-code platforms, then the platform can maintain proprietary control and specific functionality, but developers face steep learning curves and productivity bottlenecks
Solution Approach 1:
The patent introduces a generative AI assistant as an intermediary between the user and the low-code platform. This assistant translates natural language requirements into platform-specific code and configurations, eliminating the need for developers to directly learn proprietary programming languages while maintaining the platform's proprietary functionality and control mechanisms
2Adaptability or versatility
If complex workflows and unfamiliar tools are used in low-code platforms, then the platform can achieve advanced functionality, but usability deteriorates and learning curves increase
Solution Approach 1:
The patent replaces the mechanical interaction model (where users directly manipulate complex workflow tools and proprietary language constructs) with an AI-based natural language interface. Users describe their needs in plain English, and the generative AI assistant automatically generates the corresponding complex workflows, thereby maintaining advanced functionality while dramatically improving ease of operation
3Ease of operation
If natural language processing capabilities are added to low-code platforms, then usability improves, but system complexity increases
Solution Approach 1:
The patent implements a multi-functional generative AI assistant that handles multiple tasks (code generation, workflow creation, data model design, integration configuration) through a single unified natural language interface. This universal approach improves usability across different platform functions while consolidating complexity into a single AI system rather than distributing it across multiple separate features
4Adaptability or versatility
If integration with existing systems and data sources is enabled, then platform versatility improves, but integration complexity increases
Solution Approach 1:
The patent enables the generative AI assistant to automatically perform integration tasks by analyzing natural language requirements and autonomously generating the necessary connection code, API integrations, and data mappings between the low-code platform and existing systems. This self-service capability allows the platform to achieve high integration versatility while keeping the user experience simple, as users do not need to manually configure complex integration parameters
Data Source
Figure 1
Figure 2
Figure 3
AI summary
In some implementations, the techniques described herein relate to a method including: receiving, by a processor, a natural language input; retrieving, by the processor, a plurality of semantically relevant results based on the natural language input; classifying, by the processor, an intent of the natural language input using a first machine learning model; selecting, by the processor, a second machine learning model based on the intent; generating, by the processor, a prompt based on a type of the second machine learning model using the natural language input and the plurality of semantically relevant results. inputting, by the processor, to prompt into the second machine learning model; obtaining, by the processor, a result responsive to the prompt from the second machine learning model; and providing, by the processor, the result to the user.