Generative AI Assistant for Low-Code Intent-Based Prompting
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Solution Overview
Problem
Low-code platforms face challenges due to steep learning curves, 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 utilizing machine learning models for natural language processing, including content generation, search and retrieval, and task automation, with a modular architecture that integrates with low-code platforms to provide intuitive interaction and seamless integration with proprietary languages and systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If proprietary or forms-based programming languages are used in low-code platforms, then platform functionality and integration capabilities are improved, but learning curve and usability worsen
Solution Approach 1:
The patent introduces a generative AI assistant as an intermediary between users and the low-code platform. The assistant translates natural language requests into platform-specific proprietary languages, eliminating the need for users to directly learn and work with complex forms-based programming languages while maintaining full platform functionality.
Solution Approach 2:
The patent replaces the mechanical approach of directly programming in proprietary forms-based languages with an AI-based system that automatically generates the necessary code. Users interact through intuitive natural language interfaces while the AI handles the complex translation to platform-specific syntax, reducing the learning curve without sacrificing functionality.
2Adaptability or versatility
If complex workflows and proprietary languages are used, then platform capabilities are enhanced, but interface intuitiveness and natural language processing worsen
Solution Approach 1:
The generative AI assistant serves as a mediator that bridges the gap between simple user interactions and complex platform capabilities. It translates intuitive natural language requests into complex workflow configurations and proprietary language code, maintaining platform capabilities while simplifying the user interface.
Solution Approach 2:
The patent segments the system into two distinct layers: a simple user-facing layer that accepts natural language inputs and a complex platform layer that executes proprietary workflows. The AI assistant operates as a translator between these layers, allowing the interface to remain intuitive while the platform maintains its full capability complexity.
3Adaptability or versatility
If integration with existing systems is implemented, then functionality is improved, but integration complexity worsens
Solution Approach 1:
The generative AI assistant acts as an intermediary that automatically handles integration complexity. Users can request integrations with existing systems through natural language, and the AI translates these requests into the appropriate platform integration workflows and API calls, eliminating the need for users to manually manage integration complexity.
Solution Approach 2:
The AI assistant performs preliminary actions by automatically generating and configuring integration connections before users need to use them. It pre-configures API parameters, authentication mechanisms, and data mapping based on the integration requirements, reducing the overall integration complexity burden on users.
4Ease of operation
If natural language processing capabilities are added, then usability is improved, but system complexity worsens
Solution Approach 1:
The generative AI assistant is positioned as an intermediary service that handles natural language processing externally to the core platform. This allows the platform to gain improved usability through natural language interfaces without directly managing the complexity of NLP systems, as the AI assistant absorbs this complexity in a separate layer.
Data Source
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.