Inference Pattern Engine for Consistent LLM Output Mapping
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Solution Overview
Problem
Large language models (LLMs) are prone to hallucinations and are computationally expensive, leading to inconsistent outputs and inefficiencies with larger input sizes, which affects cost and speed.
Innovation Solution
An inference pattern engine is used to derive patterns from LLM responses, reducing reliance on LLMs for generating outputs, thereby minimizing hallucinations and response times by leveraging beacon nodes and tree data structures to map variables to webpage or application elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLM is used to generate responses, then functional tasks can be performed, but hallucinations occur and output consistency deteriorates
Solution Approach 1:
An inference pattern engine is introduced as an intermediary between the LLM and the final output. The LLM generates initial responses, which are then processed by the inference pattern engine that extracts structured patterns and mappings. This intermediary layer filters out hallucinations and ensures consistent output formatting while preserving the LLM's functional capabilities.
Solution Approach 2:
The system creates a copy of the LLM's response through pattern extraction, generating a structured representation that captures the essential information without the hallucinations. The inference pattern engine copies the meaningful patterns from unstructured LLM output into a reliable, consistent format that can be verified and reused.
2Adaptability or versatility
If LLM is used to generate responses, then functional tasks can be performed, but computational cost and response time increase
Solution Approach 1:
The inference pattern engine performs preliminary action by extracting and storing patterns from LLM responses in advance. Once patterns are extracted and cached, subsequent queries can be answered by matching against stored patterns rather than generating new responses from the LLM, dramatically reducing response time while maintaining functional capability.
Solution Approach 2:
The system uses periodic action by alternating between LLM-generated pattern extraction (periodic training phase) and pattern-matching inference (efficient execution phase). The LLM is invoked periodically to learn new patterns, while routine queries are handled efficiently by matching against the accumulated pattern library.
3Adaptability or versatility
If LLM is used to generate responses, then functional tasks can be performed, but scalability with larger input sizes deteriorates
Solution Approach 1:
The system segments the response generation process into two independent components: pattern extraction (performed periodically by LLM) and pattern matching (performed efficiently by the inference engine). This segmentation allows the inference engine to handle large input sizes by matching against pre-extracted patterns without being constrained by LLM context window limitations, improving scalability.
Data Source
AI summary
An input and processed content associated with a tree data structure is received. It is determined that a correctness associated with a derived pattern mapping associated with a webpage or application is greater than a confidence threshold. The derived pattern mapping that is based on a large language model response is obtained. The derived pattern mapping is utilized to generate a response for the input.