Arun Mishra

Guest Author

Arun Mishra is an Enterprise Architect and Senior Manager with 16 years of experience in financial services and healthcare technology, spanning event-driven architecture, real-time payments systems, cloud-native platform design, and ML-based fraud detection. He holds various certifications and rebuilt broken production pipelines, recovered stalled post-merger migrations, and designed payments and AI systems at scale.He writes at arunkmishra.com.

Confident AI

An eval harness found what qualitative review couldn't: AI models are most confident when wrong

There is a step in the development process for large language model (LLM)-assisted tooling that most teams skip because it's tedious, time-consuming, and doesn't produce results visible to end users: Verifying that what the model is saying is actually correct. Not fluent, not coherent, not topically relevant — correct in the sense of accurately identifying the right answer to the specific problem the tool was built to solve.