Stateful GPT Prompt Construction for Context Consistency
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Solution Overview
Problem
Generative response engines lack personalization and context retention, leading to inconsistent outputs due to statelessness and long prompts that can deviate from the intended task.
Innovation Solution
Implementing a stateful GPT system with dynamic prompt construction and a stateful prompt generator to manage system prompts based on user interactions, maintaining context and transitioning between defined states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a stateless generative response engine uses long prompts to maintain context, then it can process complex queries, but the prompts may deviate from the intended task and outputs become inconsistent
Solution Approach 1:
The patent segments the prompt into a system prompt and user prompt, where the system prompt is dynamically generated based on the current state and the user prompt contains the actual query. This segmentation allows the system to maintain consistent context through state management while processing complex user queries without deviation.
Solution Approach 2:
The system implements dynamic prompt construction where the system prompt is regenerated based on the current state at each turn. This dynamic adaptation ensures that the prompt remains aligned with the intended task and current conversation context, preventing deviation and maintaining output consistency across multiple turns.
2Ease of manufacture
If the generative response engine operates without state management, then it is simpler to implement, but it lacks personalization and context retention
Solution Approach 1:
The system incorporates feedback mechanisms where the current state is used to generate the system prompt, which then influences the model's response. This feedback loop ensures that context and personalization information are retained and utilized in subsequent turns, addressing the information loss problem while maintaining reasonable implementation complexity.
3Manufacturing precision
If the system uses dynamic prompt construction based on state, then response coherence and accuracy improve, but the system complexity increases
Solution Approach 1:
The patent introduces a state management component as an intermediary that handles the complexity of tracking context and generating appropriate system prompts. This intermediary layer abstracts the complexity from the main generative model, allowing the system to achieve high response accuracy through state-aware prompt construction without directly increasing the model's complexity.
Data Source
AI summary
Disclosed are systems, apparatuses, processes, and computer-readable media for a stateful generative pretrained transformer (GPT) in a generative response engine. The present technology includes receiving, by a generative response engine, a first user prompt from a client device, wherein the generative response engine is associated with a first state; determining, by the generative response engine using a first system prompt associated with the first state, the first user prompt corresponds to a second state different from than the first state; generating a second system prompt associated with the second state, wherein a tool of the generative response engine retrieves information associated with the second state to combine into the second system prompt; obtaining, by the generative response engine, a first response based on the first user prompt and the second system prompt associated with the second state; and providing the first response to the client device.