Evaluate with evidence

Track for local LLMs: PHI exfil test pass, note rubric vs cloud baseline, tokens/sec, crash rate, patch lag. Report fractions like 36/40, not vague quality adjectives. Segment by language, plan tier, document length, or other slices that matter for the offline clinical note assistant.

Freeze inputs and neighboring versions while evaluating local LLMs. Change one control. Pair results case by case on the offline clinical note assistant. Define hard gates (severe errors, privacy, latency) before the bake-off. Use deterministic checks where possible; humans for nuance; model judges only with calibration against gold.

Numeric reminder for local LLMs: A 7B 4-bit model may fit in ~5–6 GB RAM yet lose accuracy on rare clinical abbreviations—measure on your notes, not only tokens/sec.

Resist adding a twelfth metric before the first three for local LLMs on the offline clinical note assistant have owners. This workload improves faster when a small scorecard is trusted than when a warehouse of unused plots exists.

Also pin one numeric memory from this local LLMs chapter: A 7B 4-bit model may fit in ~5–6 GB RAM yet lose accuracy on rare clinical abbreviations—measure on your notes, not only tokens/sec. That number is not decoration; it is a template for how claims about local LLMs on the offline clinical note assistant should look in design docs. Scoped specifically to local LLMs / offline clinical note assistant / evaluation.

People confuse local LLMs with neighboring buzzwords when debugging the offline clinical note assistant. Before changing prompts, ask whether the broken stage was evidence gathering, the local LLMs judgment itself, validation, or the product action. Fixing the wrong stage creates folklore (“we tried local LLMs and it failed”) that blocks the next team on the offline clinical note assistant. Scoped specifically to local LLMs / offline clinical note assistant / evaluation.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to local llms rather than generic AI advice.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to local llms rather than generic AI advice.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to local llms rather than generic AI advice.