AI POD Orchestration for Reliable Enterprise App Generation
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Solution Overview
Problem
Existing application development processes are time-consuming and inefficient, often requiring extensive redevelopment due to ideation changes, and integration of Generative Artificial Intelligence (GenAI) tools leads to errors and complexity, lacking in creating multi-user enterprise-grade applications.
Innovation Solution
An AI-based system and method utilizing digital Product Oriented Delivery (PODs) with AI personas that leverage Large Language Models (LLMs) to iteratively generate and deploy applications, ensuring precision and efficiency through a cognitive environment, integrating advanced LLMs transformed into Large Action Models (LAMs) to mimic human development teams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Generative AI tools are used for feature development through continuous interaction, then code generation capability is improved, but integration errors increase and require additional prompting to rectify
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between the generative AI tool and the codebase. This intermediary validates, verifies, and coordinates AI-generated code before integration, preventing errors from propagating into the main codebase. The intermediary serves as a buffer that filters out problematic generated code while allowing valid code to pass through, thus resolving the contradiction between high code generation capability and high error rates.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors AI-generated code, evaluates its quality and compatibility, and provides feedback to the generative AI tool. This feedback loop enables the system to learn from integration errors and improve future code generation, reducing the frequency and severity of integration errors while maintaining high productivity.
2Productivity
If iterative prompting approach is used with GenAI tools, then code generation is achieved, but time consumption increases to approximately 90% of manual development time
Solution Approach 1:
The patent applies preliminary action by pre-configuring the system with a curated library of validated code templates, patterns, and best practices before the actual code generation process begins. This preparation work enables the generative AI tool to produce higher-quality code on the first attempt, reducing the need for iterative prompting and significantly cutting down development time while maintaining automated generation capabilities.
Solution Approach 2:
The patent dynamically adjusts parameters such as temperature, top-k sampling, and prompt complexity based on the specific development context and requirements. By optimizing these parameters in real-time, the system achieves better code generation quality with fewer iterations, thereby reducing the 90% time consumption associated with iterative prompting while preserving automated generation benefits.
3Productivity
If chatbot implementation of LLMs is used for code generation, then code output is produced, but context retention of exact terms and definitions from earlier codes is lost
Solution Approach 1:
The patent introduces an intermediary component that maintains a persistent context window and term dictionary throughout the code generation process. This intermediary captures and stores exact terms, definitions, and contextual information from earlier generated codes, making them available for subsequent generation tasks. It acts as a memory bridge that connects different code generation sessions, preventing context loss while maintaining fast generation speeds.
Solution Approach 2:
The patent implements a nested structure where multiple levels of context management are embedded within the code generation system. At the innermost level, the LLM generates code quickly; at intermediate levels, context validation and term tracking occur; at outer levels, overall project context is maintained. This nested architecture enables the system to retain contextual information across multiple generation iterations without significantly impacting generation speed.
4Device complexity
If existing technologies create single stack or maximum two stack applications, then development simplicity is maintained, but enterprise-level multi-user application capabilities are insufficient
Solution Approach 1:
The patent implements a universal multi-stack architecture that can dynamically adapt to support multiple technology stacks (frontend, backend, database, mobile, etc.) within a single unified system. This multi-functional architecture enables the system to handle complex enterprise-level multi-user applications while maintaining a simplified development interface. The system can selectively activate only the necessary stacks for each project, preserving simplicity while providing enterprise-grade capabilities when needed.
Data Source
AI summary
Artificial Intelligence-based System and Method for Generating and Deploying Applications via a Cognitive Environment System and method for generating and deploying applications via cognitive environment is provided. Digital PODs comprising digital AI personas are triggered in response to system prompt for evaluating first set of features from input data using LLMs based on a predefined function to generate first outcome. First outcome is generated based on external data retrieved from external tools. First outcome is compared with input data and comparison is inputted to LLMs as prompt template. LLMs evaluate whether to proceed with next step in a series of steps associated with subsequent predefined functions to arrive at final outcome. Next step involves generating second outcome by extracting second set of features from first outcome and applying subsequent predefined function over first outcome. Series of steps are implemented till LLMs iteratively determine that the final outcome is comparable to desired outcome.