Language Model Pre-Prompt Engineering for Relevant AI Responses
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Solution Overview
Problem
Existing generative AI language models often generate irrelevant or fabricated responses due to a lack of user and operator-specific prompts, leading to reduced user satisfaction and ineffective goal alignment.
Innovation Solution
A system that generates user- and operator-specific prompts using layered database queries, incorporating user and operator preferences to modify the system prompt for a machine-learning language model, thereby enhancing relevance and reducing fabrications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a standard system prompt is used without user-specific customization, then the system operation is simple, but the relevance of generated responses to user preferences deteriorates
Solution Approach 1:
The system performs preliminary actions by generating and storing customized system prompts for each user based on their profile information before actual language generation occurs. User-specific system prompts are created in advance using their preferences, demographics, and interaction history, so that when users interact with the language model, the customized prompts are already ready to ensure relevant responses without adding complexity to the real-time interaction process.
2Adaptability or versatility
If user-specific information is retrieved and integrated into prompts, then the relevance of responses improves, but the processing time and computational resources increase
Solution Approach 1:
User-specific system prompts are generated in advance using pre-fetched user profile information from databases, eliminating the need to retrieve and process user data during real-time interactions. The system prepares customized prompts beforehand based on user demographics, preferences, and interaction history, so that during actual language generation, only the pre-prepared customized prompt needs to be provided to the language model, significantly reducing processing time.
3Reliability
If operator preferences are incorporated into system prompts, then the alignment with operational goals improves, but the system complexity increases
Solution Approach 1:
The system merges operator preferences with user-specific information in a unified system prompt structure. Operator preferences regarding response format, tone, and content guidelines are integrated together with user profile data, demographics, and interaction history into a single customized system prompt. This consolidation ensures alignment with operational goals while maintaining a streamlined prompt generation process that doesn't require separate handling of multiple preference layers.
Data Source
AI summary
A method of automatic pre-prompt generation includes receiving at least one user preference from a user device, modifying a system prompt for a machine-learning language model based on the received at least one user preference to generate a modified system prompt, providing the modified system prompt as an initial input to the machine-learning language model, receiving a natural-language text prompt provided by the user to a chat application on the user device, receiving a user identifier from the user device, querying a first database with the user identifier to retrieve first information, generating a representation of the first information and the natural-language prompt, querying a second database using the representation to retrieve second information, and generating a modified text prompt based on the natural-language prompt, the first information, and the second information.