Natural Language Assistant Prompting for Context-Rich Analytics Insights
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Solution Overview
Problem
Conventional natural language processing systems generate rigid, template-based outputs that lack flexibility and contextually enriched insights, failing to provide accurate and engaging responses to nuanced user queries.
Innovation Solution
A system employing a natural language assistant with large language models and an associative engine that uses prompt engineering, narrative templates, and hypercube definitions to generate flexible, contextually enriched insights, incorporating real-time data context and user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional template-based NLP systems are used, then system complexity is reduced and ease of manufacture is improved, but the output flexibility and contextual enrichment capability deteriorate
Solution Approach 1:
The patent introduces an intermediary layer between the user query and the template-based system. This intermediary includes a context extraction module that identifies relevant entities, relationships, and contextual information from the user's natural language query, and a prompt generation module that transforms this extracted context into structured prompts for the language model. This intermediary layer enables flexible, contextually-enriched outputs without requiring complete redesign of the underlying template system.
Solution Approach 2:
The system segments the NLP processing into distinct functional modules: context extraction, prompt generation, language model inference, and result integration. Each module handles a specific aspect of the processing pipeline, allowing the system to maintain complexity in only the necessary areas while keeping other components simple and template-based. This segmentation enables gradual adoption and easier maintenance.
2Loss of information
If large language models with prompt engineering are implemented, then contextual enrichment and engagement quality are improved, but computational resource consumption and processing time increase
Solution Approach 1:
The system performs preliminary actions by extracting and structuring contextual information from user queries before submitting prompts to the language model. The context extraction module identifies and organizes relevant entities, relationships, and data points in advance, creating a condensed representation that captures the essential contextual information. This preliminary processing reduces the complexity and size of the prompts sent to the LLM, thereby reducing computational resource consumption while maintaining contextual enrichment quality.
Solution Approach 2:
The patent extracts only the most relevant contextual information from user queries and associated data sources, rather than processing or transmitting all available information. The context extraction module selectively identifies key entities, relationships, and data points that are directly relevant to answering the user's query, filtering out redundant or less important information. This extraction approach maintains high contextual enrichment while reducing the computational burden on the language model.
3Reliability
If narrative templates are used to guide language models, then response accuracy and consistency are improved, but the natural language interaction flexibility deteriorates
Solution Approach 1:
The system dynamically adapts the use of narrative templates based on the specific query and extracted context. Rather than applying fixed templates rigidly, the prompt generation module selects and customizes templates to match the query type and contextual information. The language model itself provides dynamic generation of natural language responses, which are then refined using template-based structures. This dynamic approach maintains response accuracy through template guidance while preserving interaction flexibility through adaptive template selection and LLM-generated natural language.
Data Source
AI summary
Methods and systems for improved natural language processing and generation of insights are described herein. Aspects of machine learning and natural language processing may be employed to interpret user queries and provide insights, recommendations, and visualizations. The system employs large language models and machine learning services to enhance the natural language interaction between users and their analytics.