Skill-Aligned Inference Model Context Selection for Reliable Responses
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Solution Overview
Problem
Existing inference models generate outputs that are unreliable and outdated due to the use of poor-quality training data, leading to negatively impacted computer-implemented services, particularly when the generated inferences do not align with the user's skill level, resulting in suboptimal user experience and service quality.
Innovation Solution
A method to manage inference models by identifying user skill levels and selecting relevant data sources with corresponding skill levels to create ingest data packages, using a retrieval-augmented generation pipeline, ensuring the models generate contextually appropriate and relevant inferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If inference models use general training data without considering user skill levels, then the model can generate a broad range of inferences, but the relevance and quality of inferences for specific users deteriorates
Solution Approach 1:
The patent segments the training data into multiple data sources, each associated with different skill levels. The system divides the data selection process into discrete steps: identifying user skill level, selecting corresponding data sources, and generating inferences tailored to that skill level. This segmentation allows the model to adapt to different user needs without requiring a complete redesign of the inference system.
Solution Approach 2:
The patent implements preliminary action by pre-associating skill levels with data sources before the inference process. The system预先 identifies and categorizes data sources by skill level, and pre-determines the user's skill level before generating inferences. This preliminary organization enables rapid, context-appropriate data selection during actual inference without adding complex real-time decision-making.
2Reliability
If inference models use outdated or poor-quality training data, then the model training process becomes simpler, but the reliability and up-to-dateness of generated inferences deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors and evaluates the quality, relevance, and up-to-dateness of training data sources. User interactions and inference outcomes provide feedback that informs data source selection and updates. This feedback loop ensures that only high-quality, current data is used for generating inferences, maintaining reliability without requiring manual data cur curation.
Solution Approach 2:
The system performs self-service by automatically managing the complexity of data source selection and quality assessment. The inference model autonomously identifies appropriate data sources based on user skill level and data quality metrics, without requiring external intervention for data curation. This self-service capability handles the complex data management tasks internally while presenting a simple interface to users.
3Ease of operation
If the system implements skill level matching between users and data sources, then the quality of computer-implemented services improves, but the operational complexity increases
Solution Approach 1:
The patent implements dynamics by making the data source selection adaptive and flexible rather than static. The system dynamically adjusts data source selection based on identified user skill levels, allowing the same inference model to serve multiple user types effectively. This dynamic adaptation improves service quality for diverse users without requiring separate models for each skill level, managing complexity through flexibility rather than multiplication of components.
Data Source
AI summary
Methods and systems for managing an inference model are disclosed. A prompt for the inference model may be associated with a user. A user level of skill for the user may be identified based on user information for the user. The user level of skill may be used to identify at least one data source with a data source level of skill that corresponds to the identified user level of skill and that includes information relevant to the prompt. The at least one data source and the prompt may be used to generate an ingest data package. The ingest data package may be used as ingest for the inference model so that the inference model generates a response to the prompt using information from the at least one data source as context for the response.