LLM Workflow Data Structure for Dynamic Knowledge Retrieval
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Solution Overview
Problem
Organizations face challenges in storing and retrieving institutional knowledge effectively, leading to cumbersome data retrieval processes and difficulties in identifying relevant solutions for user queries.
Innovation Solution
A computing system that utilizes a large language model (LLM) to classify user queries into content clusters, construct a workflow data structure, and generate query responses based on user queries, query context data, and the workflow data structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If institutional knowledge is stored in traditional databases and computing systems, then knowledge can be stored and retrieved, but the data retrieval process becomes cumbersome and laborious
Solution Approach 1:
The patent transforms static database storage into a dynamic workflow data structure that automatically adapts to user queries. The system dynamically generates workflow representations by processing user input through trained AI models, enabling the data structure to respond flexibly to different query types and contexts rather than requiring fixed retrieval paths.
Solution Approach 2:
The patent introduces trained AI models (including language models and workflow generation models) as intermediaries between the stored institutional knowledge and user queries. These models process user input, retrieve relevant information from the workflow data structure, and generate appropriate responses, eliminating the need for users to manually navigate cumbersome database retrieval processes.
2Quantity of substance
If institutional knowledge is stored in traditional databases, then knowledge can be aggregated, but users encounter difficulties in identifying the correct solution applicable to their current issues
Solution Approach 1:
The patent segments institutional knowledge into structured workflow data with distinct components including workflow definitions, task descriptions, decision nodes, and outcome associations. This segmentation organizes knowledge into manageable, queryable units that can be efficiently retrieved and applied to specific user issues rather than searching through undifferentiated data masses.
Solution Approach 2:
The patent changes the organizational parameters of stored knowledge from traditional database schemas to workflow-specific parameters including task sequences, decision criteria, and outcome mappings. This parameter transformation enables the system to match user queries with appropriate workflow solutions based on contextual parameters rather than relying on users to manually identify relevant information.
3Reliability
If traditional data structures are used to store institutional knowledge, then data can be stored, but the system lacks dynamic and responsive capabilities to tailor responses to specific user queries and contexts
Solution Approach 1:
The patent implements dynamic workflow data structures that automatically adapt their behavior based on user queries and contextual information. The system dynamically selects and executes appropriate workflow paths by processing user input through trained AI models, enabling responsive tailoring of responses to specific user needs and contexts rather than providing static, one-size-fits-all answers.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system processes user queries, retrieves relevant workflow information, generates responses, and uses this interaction data to refine future responses. The trained AI models learn from query patterns and outcomes, continuously improving their ability to tailor responses to specific user contexts and preferences.
Data Source
AI summary
Systems and methods for generating a workflow data structure are provided. The system includes one or more processors; and one or more transitory or non-transitory computer-readable media storing instructions that are executable to cause the one or more processors to perform operations, the operations comprising: receiving input data comprising a corpus of documents, a user query, and query context data; processing the corpus of documents to generate training data; training a large language model (LLM) using the training data; classifying, using the LLM, the user query to at least one content cluster of a plurality of content clusters based on the query context data; constructing, using the LLM, a workflow data structure as a function of the classifying; and generating, using the LLM, a query response as a function of the user query, the query context data, and the workflow data structure.