LLM Query Interface With Semantic Chunking for Domain Text
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Solution Overview
Problem
Large language models struggle with domain-specific tasks such as generating text related to technology and law, often inventing inaccurate 'facts' due to their general-purpose training, and conventional text segmentation techniques disrupt semantic content, leading to low-quality responses.
Innovation Solution
A text generation interface system that processes input documents into domain-specific chunks, uses retrieval augmented generation, and incorporates these chunks into prompts for large language models, ensuring semantic integrity and reducing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If general-purpose training is used for large language models, then the model can generate text that approaches human-generated language, but the model becomes poorly equipped to handle domain-specific tasks such as technology and law
Solution Approach 1:
The patent segments the training data and model application into general-purpose language understanding and domain-specific knowledge modules. The system separates core language generation capabilities from domain-specific content, allowing the model to maintain natural language fluency while incorporating specialized knowledge through retrieval-augmented generation and domain-specific prompt engineering.
Solution Approach 2:
The patent introduces an intermediary retrieval system and prompt engineering layer between the general-purpose language model and domain-specific tasks. This intermediary component fetches relevant domain-specific information and formats it into appropriate prompts, enabling the model to handle specialized tasks without requiring domain-specific training.
2Productivity
If conventional text segmentation techniques are used to process input documents, then the text can be divided into manageable chunks, but semantic content is disrupted leading to low-quality responses
Solution Approach 1:
The patent applies preliminary action by performing smart text segmentation that identifies and preserves semantic boundaries before processing. The system analyzes the document structure, identifies complete semantic units (such as full paragraphs, sections, or conceptually complete passages), and segments text at these natural boundaries rather than using fixed-size chunking, thereby maintaining semantic integrity while enabling efficient processing.
3Ease of operation
If large language models are used for domain-specific tasks, then text generation capability is maintained, but the model invents inaccurate 'facts' due to general-purpose training
Solution Approach 1:
The patent implements feedback mechanisms where the retrieval-augmented generation system continuously verifies generated content against retrieved domain-specific sources. The system cross-checks factual claims with authoritative domain knowledge, and when discrepancies are detected, it adjusts the generation to align with verified information, thereby improving factual accuracy while maintaining text generation capabilities.
Solution Approach 2:
The patent introduces an intermediary retrieval and verification layer that sits between the language model and domain-specific fact generation. This intermediary component fetches authoritative information from domain-specific knowledge bases, validates factual claims, and ensures that generated content aligns with verified domain knowledge, preventing the model from inventing inaccurate facts.
Data Source
AI summary
Text generation prompts may be determined based on an input document and a text generation prompt template. The text generation prompts may include text from the input document and questions related to the text. The text generation prompts may be sent to a remote text generation modeling system, which may respond with text generation prompt response messages including novel text portions generated by a text generation model. The text generation prompt response messages may be parsed to generate answers corresponding with the questions.