Dynamic LLM Prompt Adaptation for Open Banking Identity Data
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Solution Overview
Problem
Existing open banking services face challenges in efficiently aggregating and managing data due to the lack of automated, dynamic, and adaptable artificial intelligence solutions, leading to manual intervention and inefficiencies in decision-making.
Innovation Solution
A computer-implemented method and system using a dynamic large language model (LLM) to automatically adapt to open banking system objectives, enabling efficient data management and decision-making by iteratively refining prompts and retraining the LLM based on performance characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used for data aggregation and decision-making in open banking services, then flexibility and adaptability are maintained, but productivity and efficiency deteriorate
Solution Approach 1:
The system enables self-service automation where the LLM autonomously performs data aggregation, analysis, and decision-making tasks without requiring manual intervention. The model automatically processes consumer identity data, matches it against training actions, and generates responses based on performance characteristics, thereby improving productivity while reducing manual workload.
Solution Approach 2:
The system implements feedback mechanisms by evaluating LLM outputs against predefined performance characteristics and using this feedback to guide further training and optimization. This continuous feedback loop enables the system to automatically improve its data aggregation efficiency and adapt to changing requirements without manual reconfiguration.
2Adaptability or versatility
If existing AI solutions are used for open banking services, then some automation is achieved, but adaptability to unique data structures and vernacular deteriorates
Solution Approach 1:
The system employs dynamic adaptation where the LLM continuously learns from and adjusts to unique data structures and vernacular in open banking services. The model dynamically updates its understanding based on encountered data patterns, enabling it to adapt to evolving data formats and terminology without requiring manual programming for each scenario.
Solution Approach 2:
The system changes operational parameters by retraining the LLM based on performance characteristics and encountered data patterns. This parameter adjustment enables the model to adapt to different data structures and vernacular by modifying its internal processing parameters rather than requiring rigid pre-programming for each data format.
3Productivity
If consolidated dynamic automation is implemented, then productivity improves, but device complexity increases
Solution Approach 1:
The system achieves multi-functionality by using a single LLM-based platform to perform multiple tasks including data aggregation, analysis, decision-making, and adaptation to various data structures. This universal approach consolidates what would otherwise require multiple separate systems into one unified platform, improving productivity while managing complexity through functional integration rather than multiplication of components.
Data Source
AI summary
A computer-implemented method for providing dynamic LLM open banking services that includes: generating a predefined training action and a predefined prompt modification for consumer identity data prompts and storing with respective metadata configured for matching against values for performance characteristics of the LLM; generating an output based on a first prompt for consumer identity data to the LLM; evaluating the output against the predefined performance characteristics to generate values; matching the values to the predefined training action and prompt modification using the metadata; based on the predefined training action, curating a training data set and retraining the LLM thereon to generate a retrained LLM; based on the predefined prompt modification, generating a second prompt seeking consumer identity data; and generating a second output based on the second prompt to the retrained LLM.