Evaluate with evidence

Track for fine-tuning vs RAG: citation support, freshness lag, style rubric, factual error rate, $/query. Report fractions like 36/40, not vague quality adjectives. Segment by language, plan tier, document length, or other slices that matter for the legal research assistant.

Freeze inputs and neighboring versions while evaluating fine-tuning vs RAG. Change one control. Pair results case by case on the legal research 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 fine-tuning vs RAG: If regulations change weekly, a 2-week fine-tune cadence cannot beat an index refreshed hourly on citation freshness.

Resist adding a twelfth metric before the first three for fine-tuning vs RAG on the legal research 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 fine-tuning vs RAG chapter: If regulations change weekly, a 2-week fine-tune cadence cannot beat an index refreshed hourly on citation freshness. That number is not decoration; it is a template for how claims about fine-tuning vs RAG on the legal research assistant should look in design docs. Scoped specifically to fine-tuning vs RAG / legal research assistant / evaluation.

People confuse fine-tuning vs RAG with neighboring buzzwords when debugging the legal research assistant. Before changing prompts, ask whether the broken stage was evidence gathering, the fine-tuning vs RAG judgment itself, validation, or the product action. Fixing the wrong stage creates folklore (“we tried fine-tuning vs RAG and it failed”) that blocks the next team on the legal research assistant. Scoped specifically to fine-tuning vs RAG / legal research 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 fine tuning vs rag 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 fine tuning vs rag 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 fine tuning vs rag rather than generic AI advice.