LLM Routing Explanations With Immutable Audit Trails
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Solution Overview
Problem
Current large language model (LLM) routing systems lack transparency and comprehensive audit trails, making it difficult for organizations to demonstrate compliance with regulatory requirements and understand how decisions balance cost versus quality considerations.
Innovation Solution
An explainable LLM routing architecture that provides human-readable explanations for each decision and maintains cryptographically secured audit trails, analyzing queries across multiple dimensions to ensure compliance and transparency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLM routing systems operate as black boxes to simplify decision-making, then operational speed and simplicity are improved, but transparency and auditability deteriorate
Solution Approach 1:
The patent introduces an intermediary component that sits between the black box LLM routing system and the external observers. This intermediary captures, structures, and outputs decision information in a human-readable format without interfering with the rapid operation of the underlying routing system. The intermediary translates internal decision signals into transparent audit trails, thus maintaining operational speed while recovering transparency.
Solution Approach 2:
The patent creates a copy of the decision-making information in a structured, human-readable format. Rather than modifying the black box system itself, the patent generates parallel representations of routing decisions that can be audited and understood. This copying approach preserves the original system's operational speed while providing transparent documentation of decisions for compliance purposes.
2Reliability
If comprehensive audit trails are implemented to ensure regulatory compliance, then transparency and accountability are improved, but system complexity increases
Solution Approach 1:
The patent segments the compliance and auditing functions from the core LLM routing system. By separating these concerns into distinct modular components, the patent enables comprehensive audit trails to be implemented without entangling the entire system in complexity. The routing system maintains its decision-making logic while independent audit modules capture and structure compliance information, reducing overall system complexity.
Solution Approach 2:
The patent implements preliminary action by pre-defining structured formats and schemas for audit trail data before compliance issues arise. By establishing standardized templates for capturing routing decisions, model characteristics, and selection rationale in advance, the patent simplifies the compliance documentation process. This preliminary structuring eliminates the need for complex ad-hoc compliance tracking, reducing system complexity while ensuring reliable auditability.
3Loss of information
If detailed explanations are generated for each routing decision, then transparency and understandability are improved, but computational overhead and processing time increase
Solution Approach 1:
The patent applies partial action by generating explanations selectively rather than for every single routing decision. The system identifies key decisions that require detailed documentation and generates comprehensive explanations only for those cases. For routine or low-risk routing decisions, the system uses simplified or abbreviated explanation formats. This partial approach maintains transparency where needed while avoiding the excessive processing time that would result from universal detailed explanations.
Data Source
AI summary
Systems for explainable large language model routing with immutable audit trails are disclosed. The system receives a query and determines its characteristics including complexity, domain, regulatory constraints, and performance requirements. It retrieves profiles for multiple LLMs from a model matrix containing performance attributes, resource consumption, and compliance parameters. The system selects a particular LLM by balancing resource consumption with performance requirements, evaluating regulatory compliance, ranking LLMs based on these factors, and prioritizing models with successful processing history. The system generates a human-readable explanation of the selection including decision factors, rationale, and alternatives considered. Finally, it records the selection and explanation in a tamper-evident, immutable audit trail data structure.