Foundation Model Prompt Refinement for Content Generation
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Solution Overview
Problem
Users struggle to fully exploit the capabilities of content assistants due to difficulties in articulating their intent, lack of background knowledge, or confidence in writing, leading to suboptimal use of foundation models for content generation.
Innovation Solution
A computing device integrates a foundation model by generating prompts that include natural language input from users, task associations, and context information from documents, allowing the model to generate completions that refine and expand user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users directly input natural language to foundation models, then the process is simple, but the output quality is insufficient due to poor articulation of intent
Solution Approach 1:
The system introduces an intermediary layer between user input and the foundation model. This intermediary automatically generates contextual information, task descriptions, and refined prompts based on the user's initial input and the document content, thereby improving output quality without increasing user effort
Solution Approach 2:
The system performs preliminary processing of user input by automatically generating contextual information and task descriptions before submitting to the foundation model. This preliminary action enriches the prompt with necessary context, improving the quality of content generation while keeping the user interface simple
2Manufacturing precision
If users provide detailed context and task information, then the content generation quality improves, but the complexity of the interface increases
Solution Approach 1:
The system automatically extracts contextual information from the document and generates task descriptions based on user input, without requiring users to manually provide these details. This self-service approach maintains high content generation quality while keeping the interface simple
Solution Approach 2:
The system performs preliminary extraction of contextual information and generation of task descriptions before the user even sees the prompt interface. This preliminary action prepares rich context in advance, allowing the interface to remain simple while maintaining high output quality
3Measurement precision
If the system processes more context information, then the relevance of content suggestions improves, but the processing time increases
Solution Approach 1:
The system selectively processes and includes only the most relevant contextual information from the document based on the user's input and the task at hand. This local quality approach ensures high relevance of suggestions while minimizing unnecessary processing of irrelevant document sections
Solution Approach 2:
The system dynamically adjusts the amount and type of contextual information processed based on the specific task and user input. This parameter change allows the system to optimize between relevance and processing time by including only necessary context for each specific interaction
Data Source
AI summary
Technology is disclosed herein for content assistance processes via foundation model integrations in software applications. In an implementation, a computing device receives natural language input from a user relating to content of a document in a user interface of an application. The computing device generates a first prompt for a foundation model to generate at least a completion to the natural language input. The computing device receives a reply to the first prompt from the foundation model which includes a completion to the natural language input. The computing device causes display of the completion in association with the natural language input in the user interface and receives user input comprising an indication to combine the input and the completion, resulting in a revised natural language input. The computing device submits a second prompt including the revised natural language input to the foundation model.