Fine-tuning vs RAG
Anticipate failure modes
Name failures by their mechanism in fine-tuning vs RAG on the legal research assistant, not with a generic hallucination label.
1Learn the idea
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Response design
See it
Fine-tune
Teach voice / format into weights
RAG
Fetch fresh docs at ask time
Fine-tune = bake in style · RAG = look things up when answering
For each severe fine-tuning vs RAG failure on the legal research assistant, define stop condition, safe state, owner, and lasting prevention. Rollback only works if prior prompts, indexes, and models remain available. “Send to a human” needs queue capacity and context—not just a button name.
Run one tabletop on the legal research assistant for fine-tuning vs RAG: inject a defect, verify detection, contain, recover, and keep the blameless trace.
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Make it operational
After the tabletop, store the injected fine-tuning vs RAG defect for the legal research assistant as a regression fixture. If the same failure later reaches users silently, your detection story was aspirational. Detection without a fixture tends to rot for fine-tuning vs RAG.
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 / failure-modes.
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Common mix-ups
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 / failure-modes.
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Rehearsal (fine-tuning-vs-rag/failure-modes)
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.
Read
Rehearsal (fine-tuning-vs-rag/failure-modes)
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.
Read
Rehearsal (fine-tuning-vs-rag/failure-modes)
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.
Go deeper
Before you start
Why this matters
Invent an incident for the legal research assistant involving fine-tuning vs RAG. What earliest signal should fire before users complain?
Facts in weights
Detect with model invents repealed clauses. Respond by move facts to retrieval.
RAG without style control
Detect with answers correct but unusable tone. Respond by light tune or better prompts.
Stale index + tuned confidence
Detect with fluent outdated law. Respond by freshness monitors; dates in chunks.
Train/test leak in tune set
Detect with inflated offline scores. Respond by strict doc separation.
In the wild
See how this idea shows up as a product and a company — then come back to the lesson. Skills transfer across vendors.
Related lessons
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Page assessment
Answer from memory. Completion is saved from this evidence, not from opening the next page.
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