Schema-Guided LLM Interface for Large Dataset API Calls
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Solution Overview
Problem
Large Language Models (LLMs) face limitations in processing large data sets and generating accurate, multimodal outputs such as images and graphics, prone to hallucinations and inefficient computational resource usage due to input constraints and lack of endpoint understanding.
Innovation Solution
An LLM interface layer determines a schema for data sets, generates prompts to guide LLMs in processing specific data types and visual content, reducing computational load by offloading tasks to external endpoints and minimizing hallucinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If LLMs process large data sets directly, then comprehensive analysis is achieved, but computational resource consumption increases and input constraints are violated
Solution Approach 1:
The patent segments the data processing task by introducing an LLM interface layer that separates data retrieval (handled by external systems) from data analysis (handled by LLM). This allows the LLM to work with structured schemas and prompts rather than raw large datasets, reducing computational load while maintaining analysis capability.
Solution Approach 2:
The patent introduces an LLM interface layer as an intermediary between the LLM and external data sources. This layer handles schema determination, prompt generation, and response processing, allowing the LLM to interact with large datasets indirectly through structured interfaces rather than processing the entire dataset directly.
2Adaptability or versatility
If LLMs generate multimodal outputs, then functionality is enhanced, but accuracy decreases due to hallucinations
Solution Approach 1:
The patent applies preliminary action by determining schemas and generating prompts before the LLM generates responses. This pre-processing step structures the input data and guides the LLM, reducing the likelihood of hallucinations while enabling multimodal output generation.
Solution Approach 2:
The patent implements feedback mechanisms where the LLM interface layer processes responses from the LLM, validates them against schemas, and can request regeneration if hallucinations are detected. This feedback loop improves accuracy while maintaining multimodal generation capability.
3Adaptability or versatility
If LLM input space is expanded to accommodate large datasets, then processing capability improves, but computational resources required increase
Solution Approach 1:
The patent applies local quality by providing the LLM with localized, relevant information through structured schemas and targeted prompts rather than requiring the entire dataset to be in the input space. This allows the LLM to focus computational resources on analyzing specific relevant portions of data.
4Adaptability or versatility
If LLMs are modified to process large datasets, then functionality improves, but model complexity increases
Solution Approach 1:
The patent uses an LLM interface layer as an intermediary that handles the complexity of data processing, schema determination, and prompt generation. This keeps the LLM model itself unchanged and relatively simple, while the interface layer manages the complexity of interacting with large datasets and generating multimodal outputs.
Data Source
AI summary
Various embodiments of the technology described herein cause an LLM to intelligently process data based on a user query and a schema determined for a data set. Certain embodiments programmatically leverage an LLM and utilize its output based on a user query. In this manner, data is processed without the LLM having to access an entire data set, and instead only utilizes information associated with the user query and the schema. The schema comprises a textual description, such as a string of alphanumeric characters, that describes the data, data types, and/or data structure of the data set. Embodiments of the technology described herein are performed by an LLM interface layer separate from a user device layer and an LLM layer. The LLM interface layer is positioned between an LLM abstraction layer and an application layer by which a user can interface with the LLM interface layer.