Prompt Pipeline Configuration for Flexible Multi-Model RAG
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Solution Overview
Problem
Conventional Retrieval-Augmented Generation (RAG) architectures in generative AI systems are limited by fixed retrieval criteria, making them inflexible in responding to diverse enterprise requirements and complex user queries, necessitating a more dynamic and adaptable framework for optimal data processing and response generation.
Innovation Solution
A method and apparatus for constructing a pipeline by configuring prompt layers, modules, and units based on the availability, cost, and performance of AI models, enabling automated selection and configuration of optimal combinations to meet user-specific requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional RAG architectures with fixed retrieval criteria are used, then system simplicity is maintained, but flexibility and adaptability to diverse enterprise requirements deteriorate
Solution Approach 1:
The system segments the prompt processing into multiple hierarchical levels: prompt units (individual instructions), prompt modules (groups of related units), and prompt layers (collections of modules). This segmentation allows flexible reconfiguration of the pipeline by selecting and combining specific units, modules, and layers based on enterprise requirements, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The system implements dynamic pipeline configuration where the structure and composition of prompt layers, modules, and units can be adjusted based on specific enterprise needs and query types. This dynamic adaptability allows the system to optimize performance for different scenarios while maintaining a manageable configuration framework through automated selection algorithms.
2Adaptability or versatility
If multiple AI models are integrated to meet diverse requirements, then system versatility improves, but system complexity and difficulty of management increase
Solution Approach 1:
The system creates a universal prompt pipeline framework that can accommodate multiple AI models and various enterprise requirements through a standardized structure of prompt units, modules, and layers. This universal framework enables different AI models to be integrated and managed through a common interface, reducing the complexity of multi-model integration while maintaining high versatility.
3Productivity
If automated pipeline construction is implemented, then productivity and response time improve, but system complexity increases
Solution Approach 1:
The system implements automated pipeline construction that performs self-service by automatically selecting and configuring appropriate prompt units, modules, and layers based on the input query and enterprise requirements. This automation reduces manual configuration effort and accelerates pipeline construction while managing complexity through algorithmic selection rather than manual design.
Solution Approach 2:
The system prepares prompt units, modules, and layers in advance as pre-configured building blocks that can be quickly assembled into pipelines. This preliminary preparation of modular components enables rapid pipeline construction when needed, improving productivity while keeping the complexity management straightforward through reuse of pre validated configurations.
Data Source
AI summary
An embodiment relates to a method for providing responses through a prompt pipeline structure, and more particularly, a pipeline construction method based on combinations of prompt units. The method comprises: configuring one or more prompt layers selected from among candidate artificial intelligence models based on at least one of availability, cost, and performance of each artificial intelligence model; configuring, for each layer, a set of prompt modules according to the selected artificial intelligence model; and constructing a pipeline by selecting, from among preset candidate prompt units for each of the prompt modules included in the prompt module set, one or more prompt units satisfying specific conditions, and configuring, based on the selected prompt units, combinations of prompt units for the respective prompt modules included in the prompt module set.