FDE & AI Engineering Topics

AI rarely fails because the model is bad — it fails in the last mile. This series maps the gap between prototype and production: messy data, unclear ownership, trust, rollback, and everything that kills AI in the real world.

The complete system for cracking FDE interviews — ambiguous problem framing, system design under uncertainty, discovery questions, trade-off communication, and the mental model that separates candidates who get offers from those who freeze.

Building agentic AI systems that work beyond the demo — multi-step reasoning, tool use, evals, observability, failure modes, and the engineering decisions that separate a proof-of-concept from a system customers trust.

LLMs, RAG, fine-tuning, inference optimization, and the full engineering stack for AI in production — written for engineers who want to understand the systems, not just call the API.