Combined Prompt Generation for Accurate Language Model Interactions
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Solution Overview
Problem
Existing systems face challenges in efficiently interacting with language models (LMs) due to the need for multiple prompts, specialized knowledge, and computational expense, leading to time-consuming and resource-intensive interactions.
Innovation Solution
Systems and methods that facilitate targeted interactions with LMs by generating combined prompts using context and session interactions, reducing the number of prompts required and improving response accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple prompts are used to interact with language models, then response accuracy can be improved, but computational expense and time consumption increase
Solution Approach 1:
The system performs preliminary actions by generating and storing multiple possible responses (ranked responses) in advance during the first interaction. These pre-generated responses are stored in session memory and can be directly reused in subsequent interactions, eliminating the need to re-process identical or similar queries through the language model again, thus reducing computational expense while maintaining response accuracy
Solution Approach 2:
The system merges the current user prompt with previously generated ranked responses from session memory to create a combined prompt. This merging allows the system to leverage existing high-quality responses while incorporating new user intent, reducing the need for complete re-generation and lowering computational costs
2Measurement precision
If multiple prompts are used to interact with language models, then response accuracy can be improved, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by generating and storing multiple possible responses (ranked responses) in advance during the first interaction. These pre-generated responses are stored in session memory and can be directly reused in subsequent interactions, eliminating the need to re-process identical or similar queries through the language model again, thus reducing computational expense while maintaining response accuracy
Solution Approach 2:
The system implements skipping by checking session memory for existing ranked responses before submitting new prompts to the language model. When matching responses are found in session memory, the system skips the time-consuming model inference process entirely and directly returns the pre-generated response, significantly reducing time consumption for repetitive or similar queries
3Measurement precision
If specialized knowledge is required for prompt engineering, then response accuracy improves, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service by automatically managing the complex prompt engineering tasks. The system autonomously generates combined prompts by merging user inputs with context from session memory, automatically ranks multiple possible responses, and manages session state. This eliminates the need for users to possess specialized prompt engineering knowledge while maintaining high response accuracy through sophisticated automated prompt construction and response ranking mechanisms
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.