AI Application Performance Tracking With LLM Logger Plugins
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Solution Overview
Problem
Existing log management tools lack the ability to effectively log various AI application parameters required for performance tracking, making it difficult for developers and data scientists to analyze the performance of enterprise AI applications due to rapid changes in libraries and tools across vendors and open-source communities.
Innovation Solution
A system and method that utilize a scanning process to identify application programming interfaces (APIs) from an API dictionary, generate a logging configuration file, and create a logger plug-in using fine-tuned large language models (LLMs) to instrument the AI application for logging and tracking performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional log analysis tools are used to inspect logs, then manual query-level matching or rule-based policies can be applied, but it becomes impractical to analyze the huge volume of logs generated by modern software systems
Solution Approach 1:
The patent introduces log intelligence algorithms as an intermediary between raw log data and analysis results. These algorithms automatically process and interpret logs, acting as a mediator that transforms unstructured log volume into actionable insights without requiring manual inspection of each log entry.
Solution Approach 2:
The patent replaces manual log analysis (mechanical human effort) with automated machine learning algorithms. The system uses trained models to automatically detect patterns, anomalies, and insights from logs, substituting human manual querying and rule-based analysis with intelligent automated processing.
2Quantity of substance
If existing log management tools are used, then a centralized repository for log data can be created, but these tools lack the ability to log various AI application parameters required for performance tracking
Solution Approach 1:
The patent implements dynamic logging capabilities that adapt to different AI application requirements. The system can dynamically configure which parameters to log based on the specific AI model, framework, and performance metrics needed, allowing the logging mechanism to evolve and adapt rather than using a static fixed set of loggable parameters.
Solution Approach 2:
The patent creates a universal logging framework that can handle multiple types of AI application parameters through a single unified system. The logger is designed to capture diverse parameters including model performance metrics, system resource usage, data flow information, and business logic parameters, making the logging tool versatile across different AI workloads and frameworks.
3Adaptability or versatility
If libraries and tools are rapidly expanded across vendors and open-source community, then new features and capabilities are added, but breaking changes with new versions make it difficult for developers to analyze performance
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor and adapt to library version changes. The system tracks parameter schemas, data formats, and API changes across different library versions, providing feedback to automatically adjust logging configurations and maintain consistent performance analysis despite underlying library evolutions.
Solution Approach 2:
The patent prepares for version changes by implementing version-agnostic logging patterns and maintaining backward compatibility layers. The system cushions against breaking changes by designing flexible data collection mechanisms that can accommodate future library versions without requiring complete reconfiguration, thus maintaining reliability across updates.
Data Source
AI summary
This disclosure relates generally to system and method to track performance of an AI application during development and production lifecycle. Libraries and tools that are used to develop an enterprise AI application are rapidly expanding across vendors and open-source community. These libraries are coming up with new features and breaking changes with new versions making it difficult to application developer and enterprise runtime executor. The method of the present disclosure receives an enterprise artificial intelligence (AI) application and corresponding software environment as input generate a logger plug-in using one or more fine-tuned large language models (LLMs) based on the plurality of instructions provided by the one or more instructors. Additionally, the logger plug-in is utilized to validate correctness of errors and then the logger plug-in is executed with the enterprise AI application to log and track performance of the enterprise AI application.