Building a Custom AI Governance Tool: An Inspiring Implementation Record for LLM Auditing
infrastructure#governance📝 Blog|Analyzed: Apr 24, 2026 02:58•
Published: Apr 24, 2026 01:22
•1 min read
•Zenn LLMAnalysis
This article provides a brilliantly practical approach to solving the notorious black-box problem of Large Language Model (LLM) performance in production. By building a custom auditing tool using FastAPI and Supabase, the developer creates an essential framework for tracking requests, costs, and Latency. It is a highly empowering read that transforms the abstract concept of AI governance into an accessible and actionable engineering task for individual creators.
Key Takeaways
- •Identifying accuracy drops in LLMs is incredibly difficult without a custom logging system to track the original request and response data.
- •A successful governance tool must track essential metrics like token counts, latency, cost conversion, and the specific model used.
- •Leveraging asynchronous frameworks like FastAPI perfectly complements the long waiting periods inherent to LLM processing.
Reference / Citation
View Original"However, for individual developers, AI governance is much simpler. It is simply 'understanding what is happening and creating a state where improvements can be made.'"
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