Notebook Content Generation from Selected Sources Without Manual Prompts
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Solution Overview
Problem
Current computing systems require significant user effort and computational resources to generate outputs from large language models (LLMs) due to the need for manual content switching and prompt creation, leading to inefficiencies in processing and resource utilization.
Innovation Solution
A computing device equipped with machine-learned models automatically generates summaries and performs operations on user-selected content within a notebook application, eliminating the need for manual prompt creation and reducing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual content switching and prompt creation are used to interact with LLMs, then users can query and obtain outputs from large language models, but significant user effort and computational resources are required leading to inefficiencies
Solution Approach 1:
The system performs preliminary actions by automatically selecting relevant source content and generating appropriate prompts before user interaction. The notebook application pre-processes content by organizing it into manageable units and preparing query structures, eliminating the need for users to manually switch between content sources and construct prompts from scratch.
Solution Approach 2:
The system enables self-service by allowing the LLM to automatically retrieve and process relevant content without manual user intervention. The notebook application autonomously manages content selection, prompt generation, and result delivery, reducing user effort while maintaining interaction quality.
2Adaptability or versatility
If manual content switching is performed to query LLMs about content from multiple sources, then users can access information from various documents, but computational overhead and processing time increase significantly
Solution Approach 1:
The system segments the content processing task by dividing multiple source documents into discrete notebook entries or sections. Each segment can be independently queried and processed by the LLM, allowing efficient selective retrieval without requiring the system to load and process entire document sets simultaneously, thus reducing computational overhead.
Solution Approach 2:
The notebook application serves as an intermediary layer between the user's query and the multiple content sources. It manages content retrieval, preprocessing, and organization, filtering and preparing only the relevant portions for LLM processing. This intermediary role reduces the computational burden on the LLM by pre-filtering and structuring input data.
Data Source
AI summary
A computing device for generating content includes one or more memories to store instructions and one or more processors to execute the instructions to perform operations, the operations including: providing, in response to a selection of a plurality of items of content, a user interface including a first portion and a second portion, the first portion including a summary description generated via one or more machine-learned models based on the plurality of items of content and the second portion including a plurality of user interface elements configured to perform an operation with respect to at least one of the summary description or the plurality of items of content.