Spreadsheet LLM Prompt Configuration for Relevant Low-Latency Responses
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Solution Overview
Problem
Existing spreadsheet AI models are constrained to spreadsheet data and functionalities, while large language models (LLMs) introduce latency, require significant compute power, and may generate irrelevant responses due to their broad training data.
Innovation Solution
Integrate LLMs with spreadsheet environments by generating domain-specific prompts based on user input and spreadsheet data, allowing for constrained responses that include suggestions and self-evaluations, and optimizing interactions to minimize latency and improve relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are integrated to provide general-purpose natural language processing, then the capability to generate novel and open-ended responses is improved, but latency increases and user experience deteriorates
Solution Approach 1:
The patent segments the LLM processing by creating a specialized domain-constrained version that handles spreadsheet-specific tasks. Instead of using the full general-purpose LLM for all queries, the system divides functionality between domain-specific constrained processing (for spreadsheet tasks) and general LLM processing (for other tasks), reducing latency for common spreadsheet operations while maintaining versatility for novel queries.
Solution Approach 2:
The patent applies preliminary action by pre-constraining the LLM with domain-specific knowledge and rules before processing user queries. The system pre-loads spreadsheet domain knowledge, data types, and functional constraints into the LLM context, so that when queries arrive, the LLM is already prepared and doesn't need to process general knowledge from scratch, thereby reducing latency.
2Adaptability or versatility
If LLMs are used for natural language processing, then the ability to understand and respond to diverse inquiries is improved, but compute power requirements increase significantly
Solution Approach 1:
The patent applies local quality by making the LLM's knowledge domain-specific rather than general-purpose. The system constrains the LLM to spreadsheet domain knowledge, so the model only processes and generates content relevant to spreadsheets. This localized specialization reduces the computational complexity compared to processing diverse general-domain inquiries, thereby reducing compute power requirements while maintaining high adaptability within the spreadsheet domain.
3Adaptability or versatility
If LLMs process diverse subject matter from broad training data, then the range of knowledge is improved, but the relevance of responses to specific spreadsheet contexts deteriorates
Solution Approach 1:
The patent introduces an intermediary layer between the LLM and the user query that enforces domain constraints. This intermediary system translates general LLM responses into spreadsheet-specific language and formats, and also translates user's natural language queries into domain-constrained prompts. This intermediary ensures that while the LLM can access broad knowledge, only relevant spreadsheet-specific information is processed and returned, improving response relevance.
Solution Approach 2:
The patent changes the parameters of the LLM processing by dynamically adjusting the domain constraints based on the specific spreadsheet context. The system modifies prompt parameters, temperature settings, and constraint strength according to the query type and spreadsheet data being analyzed. This parameter adjustment ensures that the LLM maintains broad knowledge access while producing highly relevant responses tailored to the specific spreadsheet context.
4Adaptability or versatility
If LLMs are deployed without domain constraints, then the creativity and open-endedness of responses is improved, but the accuracy and reliability for spreadsheet tasks deteriorates
Solution Approach 1:
The patent makes the domain constraints dynamic rather than static. The system adjusts the level of constraint based on the query type: for factual spreadsheet questions, constraints are tighter to ensure accuracy; for creative analysis or exploration queries, constraints are more permissive to allow creativity. This dynamic adjustment maintains reliability for accurate tasks while preserving creativity for exploratory tasks.
Data Source
AI summary
Technology is disclosed herein for an application service which interfaces with an LLM service. In an implementation, the application service receives a natural language input from a user associated with a spreadsheet. The application service generates a prompt based on the user input and at least a portion of the spreadsheet. The prompt constrains the LLM service to a domain specific to the spreadsheet based on the portion and submits the prompt to the LLM service. The application service receives a reply to the prompt from the LLM service and responds to the input based on the reply from the LLM service.