Engineering Philosophy
1. Evaluators Over Prompting
If you cannot deterministically measure a pipeline's performance in CI/CD, you cannot ship it. The future of AI engineering is not writing better prompts; it is writing robust programmatic judges and evaluation loops.
2. Determinism from Probability
Language models are inherently probabilistic, but enterprise systems require determinism. The architect's job is to build the structural scaffolding—strict RAG architectures, validation layers, and semantic routing—that forces probabilistic models to yield reliable, repeatable results.
3. Granular Telemetry
Massive, monolithic LLM calls fail silently. Breaking complex tasks down into micro-agents or distinct evaluation steps allows for precise telemetry. When the system fails, you must know exactly which node in the graph hallucinated.