regression

'Eval is the new system design' for LLM apps, and starting from zero data is the real test. The signal is bootstrapping a golden set, picking task-specific metrics, and running evals continuously as a regression gate.

Alignment that adds refusals and politeness often quietly degrades reasoning and coding. The alignment tax is real and measurable. Here is how to keep the safety gains without paying for them in capability.

Single-turn evals cannot catch failures that history causes, because they have no history. The offline answer is two suites, one deterministic and one simulated, and an honest account of why the simulated user will lie to you about how good your agent is.

Gaussian Processes give predictions with principled uncertainty, which is why they power Bayesian optimization. The signal is the distribution-over-functions intuition, the kernel's role, and naming the O(n cubed) wall. Here is the answer.

The textbook reflex ('drop one') is usually the wrong instinct, and whether collinearity matters at all hinges on your model and what you need from it. This is the answer that separates rote from understanding.

Everyone can recite 'linearity and normality.' Few can say which assumption matters for predictions versus inference, how to spot a violation in a residual plot, and what to actually do about it. Here is that answer.

ETA is spatiotemporal regression served in real time over conditions that change by the minute. The signal is feature freshness, segment or stage decomposition, and the asymmetric cost of being wrong. Here is the design.