Session-Based Prompt Generation for Accurate LM Interactions
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Solution Overview
Problem
Existing systems face challenges in efficiently interacting with language models (LMs) due to generic responses and the need for multiple prompts, which are time-consuming and computationally expensive, requiring specialized knowledge and complex operations.
Innovation Solution
Systems and methods that facilitate targeted interactions with LMs by generating combined prompts using context and session interactions, minimizing the number of prompts required, and reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple prompts are used to obtain specific responses from LMs, then response accuracy improves, but time consumption and computational cost increase
Solution Approach 1:
The system performs preliminary action by automatically generating optimized prompts before user interaction. The prompt generation module creates precise prompts based on user intent and historical data, eliminating the need for users to manually craft multiple prompts through iterative trial and error. This preliminary optimization reduces the number of interaction rounds required to achieve accurate responses.
Solution Approach 2:
The system introduces an intermediary component - the prompt generation module - that acts as a mediator between the user and the language model. This intermediary automatically formulates optimized prompts based on user input and historical interaction data, reducing the need for users to directly engage in multiple iterative prompting attempts and thereby reducing time consumption.
2Measurement precision
If multiple prompts are used to obtain specific responses from LMs, then response accuracy improves, but computational cost increases
Solution Approach 1:
The system performs preliminary action by automatically generating optimized prompts before user interaction. The prompt generation module creates precise prompts based on user intent and historical data, eliminating the need for users to manually craft multiple prompts through iterative trial and error. This preliminary optimization reduces the number of interaction rounds required to achieve accurate responses.
Solution Approach 2:
The system introduces an intermediary component - the prompt generation module - that acts as a mediator between the user and the language model. This intermediary automatically formulates optimized prompts based on user input and historical interaction data, reducing the need for users to directly engage in multiple iterative prompting attempts and thereby reducing computational cost.
3Measurement precision
If prompt engineering is used to tailor LM responses, then response specificity improves, but system complexity increases
Solution Approach 1:
The system implements self-service by enabling the prompt generation module to automatically create optimized prompts without requiring user expertise in prompt engineering. The system uses historical interaction data and machine learning algorithms to autonomously formulate precise prompts, eliminating the need for users to possess specialized knowledge of prompt engineering techniques.
Solution Approach 2:
The system introduces an intermediary component - the prompt generation module - that acts as a mediator between the user and the language model. This intermediary automatically formulates optimized prompts based on user input and historical interaction data, reducing the need for users to directly engage in multiple iterative prompting attempts and thereby reducing computational cost.
4Measurement precision
If multiple prompts are used to interact with LMs, then task completion accuracy improves, but token efficiency decreases
Solution Approach 1:
The system performs preliminary action by automatically generating optimized prompts before user interaction. The prompt generation module creates precise prompts based on user intent and historical data, eliminating the need for users to manually craft multiple prompts through iterative trial and error. This preliminary optimization reduces the number of interaction rounds required to achieve accurate responses.
Solution Approach 2:
The system extracts only the essential and relevant information needed for task completion from historical interaction data. The prompt generation module identifies and extracts key contextual elements from previous interactions, forming concise optimized prompts that achieve task completion with minimal token usage while maintaining high accuracy.
Data Source
AI summary
Accurate prompting improves the operation, efficiency, and output of computer models, such as language models. The disclosed systems and methods improve interaction with computer models by facilitating the generation of accurate prompts for targeted interactions with computer models. For example, disclosed systems can be configured to store prompts and responses generated with computer models in session memories. The system can use information in session memories to generate combined prompts when receiving prompts that refer to previous prompts or answers. The system improves prompt accuracy by generating combined prompts—formed by combining the context from the stored information (e.g., in context sub-prompts) and instructions in the new prompt (e.g., in instructions sub-prompts). The system can generate responses based on the combined prompts allowing the computer models to leverage the context in previous interactions, without burdensome or complicated prompts, for more meaningful or accurate responses.