Custom Instruction Injection for Context-Aware Language Models
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Solution Overview
Problem
Language models lack the ability to provide customized responses tailored to user preferences and context, leading to inconsistent interactions and a failure to adapt over time, due to the lack of structured mechanisms for processing and applying user-defined preferences.
Innovation Solution
Implementing custom model instructions through interfaces to tailor prompts and responses, which are stored in data storage associated with a user profile, and are added to the language model's system message in response to trigger events, allowing the model to determine relevance and generate personalized responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If language models use generic responses without custom instructions, then the system complexity is low, but the adaptability to user preferences deteriorates
Solution Approach 1:
The system segments custom instructions into distinct components: user profile data storage, instruction retrieval mechanisms, and integration with system messages. This modular approach allows the language model to selectively process custom instructions without requiring complete system redesign, thus improving adaptability while managing complexity through division of functions.
Solution Approach 2:
The patent introduces an intermediary layer between the user profile storage and the language model processing. This intermediary manages the retrieval, formatting, and injection of custom instructions into system messages, acting as a buffer that shields the core language model from direct complexity while enabling personalized responses.
2Ease of operation
If custom instructions are stored and retrieved for each interaction, then the personalization of responses is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-storing user preferences, profile information, and custom instructions in structured data formats during user setup phases. This advance preparation enables rapid retrieval during actual interactions, reducing processing time while maintaining high personalization quality.
Solution Approach 2:
The patent implements copying mechanisms where custom instructions are retrieved from persistent storage and injected as system messages into the language model context. This copying approach allows the model to access personalized instructions without requiring complex real-time processing or modification of the core model structure.
3Reliability
If the language model processes custom instructions as transient inputs, then the system complexity is low, but the consistency of applying user preferences deteriorates
Solution Approach 1:
The patent merges custom instructions with system messages to create a unified context that the language model processes consistently. By combining user-specific instructions with system-level guidance in a single integrated message structure, the system ensures reliable and consistent application of user preferences across all interactions without requiring separate processing pathways.
Solution Approach 2:
The system implements a universal mechanism where custom instructions are transformed into system messages that can be consistently applied across different interaction types and contexts. This multi-functional approach allows the same instruction processing framework to handle various user preferences, maintaining consistency while avoiding the need for specialized handling for each scenario.
Data Source
AI summary
Disclosed herein are methods, systems, and computer-readable media for interacting with a language model using custom instructions. In one embodiment a method includes receiving, through an interface, custom instructions, the custom instructions comprising at least one of personal information or a response type preference, storing the custom instructions temporarily within a session specific cache, in response to a trigger event, adding the custom instructions to a system message associated with the language model, the system message being a prompt modifier to the language model, in response to receiving a prompt, retrieving the custom instructions from the session specific cache, determining whether the custom instructions are relevant to the prompt, and in response to determining the custom instructions are relevant to the prompt, generating a response to the prompt based on the custom instructions.