Understand the mechanism

RAG retrieves and inserts passages; fine-tuning runs gradient updates on task examples. Hybrid systems retrieve facts and use a tuned model for tone or structure.

Read the fine-tuning vs RAG path as a pipeline for the legal research assistant. At each stage, name the representation, the owner, and how information can be lost. Identifiers must mark prompt versions, model versions, indexes, and policies so “randomness” is not the default explanation for every bug in fine-tuning vs RAG.

If regulations change weekly, a 2-week fine-tune cadence cannot beat an index refreshed hourly on citation freshness. Scoped specifically to fine-tuning vs RAG / legal research assistant / mechanism.

Keep the unit and the denominator visible when you discuss fine-tuning vs RAG. A percentage without a base, or a latency without a percentile, hides the failure mode this chapter cares about on the legal research assistant.

Learned stages estimate; deterministic stages enforce. A fluent result from the legal research assistant does not prove fine-tuning vs RAG used the right evidence. Preserve intermediates when privacy allows—candidate lists, traces, scores, citations—so you can see the first broken stage in the fine-tuning vs RAG path.

Operational correctness for fine-tuning vs RAG includes deadlines on the legal research assistant. If stages that feed this workload sum past the user’s patience, trim earlier—usually pack less, retrieve less, or parallelize—before blaming the model vendor for fine-tuning vs RAG. Mechanism diagrams that ignore time are incomplete.

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 / mechanism.

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 / mechanism.

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.