AI Project Summaries Using Deterministic Data Narratives
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Solution Overview
Problem
Project management systems face challenges in creating comprehensive and accurate project summaries that capture key insights and data, often leading to missed insights and inaccurate decision-making due to the complexity of expansive projects with multiple tasks and sub-projects.
Innovation Solution
A computer-implemented method using large language models to generate natural language project summaries through deterministic operations, applying pre-determined heuristics and prompts to derive relevant data values, and editing summaries to ensure accuracy and completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If comprehensive project data is collected and processed manually to create accurate project summaries, then the completeness and accuracy of project information is improved, but the time and resources required for creation increase significantly
Solution Approach 1:
The patent replaces manual mechanical processes of data collection, analysis, and summary writing with an automated system using large language models. The system automatically processes project data from multiple sources, applies deterministic operations to derive key values, and generates comprehensive project summaries without human intervention, thereby maintaining information completeness while dramatically reducing time consumption.
Solution Approach 2:
The system enables self-service by allowing project data to automatically generate its own summaries through the AI model. The deterministic operations and prompt generation mechanisms allow the system to autonomously process raw project data, identify key insights, and produce narrative summaries without requiring manual curation or review at each step.
2Measurement precision
If detailed analysis of all project data is performed to capture key insights, then the accuracy of project decisions is improved, but the complexity of the processing system increases
Solution Approach 1:
The patent segments the complex data processing task into distinct modular components: data ingestion from multiple sources, deterministic operations for deriving key values, prompt generation for the AI model, and summary output. This segmentation allows each component to be optimized independently while maintaining overall system accuracy without requiring excessive complexity in any single part.
Solution Approach 2:
The system introduces an intermediary layer of deterministic operations and structured prompt generation between the raw project data and the large language model. This intermediary layer pre-processes and structures the data in a way that maximizes the AI model's ability to extract accurate insights, thereby achieving high measurement precision without directly exposing the full complexity of raw data processing to the model.
3Reliability
If manual review and editing of project summaries is performed to ensure accuracy, then the reliability of project communications is improved, but the productivity of the summary generation process decreases
Solution Approach 1:
The system incorporates feedback mechanisms where the generated summaries can be automatically evaluated against the original project data to ensure accuracy. The deterministic operations provide a verifiable chain of reasoning from source data to derived values, creating an inherent feedback loop that ensures reliability without requiring manual review, thereby maintaining both reliability and productivity.
Data Source
AI summary
Systems, methods, and computer-readable media are provided for generating natural language project summaries via large language models including deterministically derived data value narratives. A computer-implemented method includes processing a first input configuring data stored in association with a plurality of fields, generating a narrative for a project, and causing display of the narrative in a report for the project. The narrative is generated by applying one or more deterministic operations to derive one or more values for the project based at least in part on at least one field of the plurality of fields, based at least in part on the configured data, generating a prompt, prompting a large language model with the prompt to generate a result, and storing the result as the narrative for the project. The prompt includes the one or more derived values and a context comprising the project for which a narrative is being generated.