Dynamic Machine Learning Prompt Hydration via Registry and Context Store
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Solution Overview
Problem
Existing machine learning models struggle with generating accurate content due to insufficient or irrelevant information, leading to inefficiencies and resource waste, and manual data provision is cumbersome and resource-intensive.
Innovation Solution
Automatically retrieve and populate prompt templates with user data based on a query, using placeholders to identify data sources, and provide the generated prompt to a machine learning model for accurate response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manually providing a machine learning model with required information, then the model can perform tasks, but the process becomes complex and tedious requiring extensive manual labor
Solution Approach 1:
The system automatically retrieves user data from electronic data sources and populates prompt templates without manual intervention. The machine learning model serves itself by having the system autonomously gather required information from designated sources and structure it into appropriate prompts, eliminating the need for users to manually compile and provide data.
Solution Approach 2:
A prompt generation system acts as an intermediary between electronic data sources and the machine learning model. This intermediary automatically retrieves data from sources, processes it through template-based prompt generation, and delivers structured information to the model, simplifying the interaction for users while maintaining system complexity management.
2Reliability
If providing a machine learning model with too little information, then the model cannot generate desired content, but providing too much irrelevant information also prevents accurate content generation
Solution Approach 1:
The system retrieves and provides only the specific user data needed for each particular task from electronic data sources. Rather than providing all available information or a fixed set of data, the prompt templates are configured to request only the locally relevant information required for that specific content generation task, ensuring appropriate data quality without excess.
Solution Approach 2:
The system dynamically adjusts the information provided to the machine learning model by changing parameters such as data sources, data types, and data volume based on the specific task requirements. Prompt templates are configured with parameters that specify exactly what information is needed, allowing the system to adapt the information provision to match task demands precisely.
3Productivity
If machine learning models generate content with errors due to insufficient information, then computing resources are wasted, but correcting these errors requires additional computing resources
Solution Approach 1:
The system performs preliminary actions by automatically retrieving and validating user data from electronic data sources before generating prompts for the machine learning model. This preliminary data gathering and verification ensures that the model receives complete and accurate information upfront, preventing errors in content generation and avoiding the need for costly corrections later.
4Measurement precision
If manually providing user-specific data for user-specific questions, then accurate responses can be generated, but the process requires extensive manual labor and computing resources
Solution Approach 1:
The system automatically retrieves user-specific data from electronic data sources associated with the user without requiring manual data collection. The prompt generation system autonomously identifies, retrieves, and structures user-specific information, enabling accurate responses to user-specific questions while eliminating the extensive manual labor previously required.
Data Source
AI summary
Aspects of the present disclosure relate to dynamically generating prompts. Embodiments include receiving a query from a user and retrieving a prompt template based on the query, wherein the prompt template comprises: natural language instructions related to providing a response to the query, and one or more placeholders associated with indications of one or more electronic data sources from which relevant user data is to be retrieved. Embodiments further include retrieving, based on the indications associated with the placeholders in the prompt template, user data associated with the user from the one or more electronic data sources. Embodiments further include populating the prompt template to produce a dynamically generated prompt by replacing the placeholders with the retrieved user data associated with the user. Embodiments further include providing the dynamically generated prompt and the query to a machine learning model that has been trained to generate content.