Generative AI Leader Fundamentals of Generative AI Practice Question
An organization wants to use a generative model to automatically generate legal contracts. The model must produce clauses that are not only grammatically correct but also legally enforceable and consistent with current jurisdiction laws. Which combination of techniques best ensures legal compliance?
⚠ Common exam trap
Generative AI Leader often tests the belief that RAG or prompt engineering alone guarantees compliance, when deterministic external validation is required for regulated domains.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Fine-tune a model on a diverse set of enforceable contracts and incorporate an external compliance verifier that uses rule-based checks.
Fine-tuning on a diverse corpus of enforceable contracts teaches the model patterns of valid legal language across contexts, while an external rule-based compliance verifier checks generated clauses against jurisdiction-specific statutes and regulations. This hybrid approach combines generative fluency with deterministic legal validation, which is necessary because LLMs alone cannot guarantee current legal compliance. The verifier provides an auditable, updatable layer that can be revised as laws change.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune a small model exclusively on legal contracts from a single jurisdiction and use it for generation.
Why it's wrong here
Fine-tuning on a single jurisdiction's contracts bakes in that jurisdiction's law and cannot adapt when laws change or other jurisdictions apply, so clauses may be unenforceable. It is tempting because fine-tuning specialises tone and clause structure, and would suit a fixed, single-jurisdiction drafting assistant where the law is stable.
- ✗
Implement retrieval-augmented generation (RAG) with a vector database of all relevant laws.
Why it's wrong here
RAG retrieves relevant statutes but the model still composes the clause, so it cannot guarantee enforceability or that retrieved law is current and correctly applied. It is tempting because RAG grounds generation in authoritative sources, and would be correct for answering legal questions with citations rather than drafting binding clauses.
- ✓
Fine-tune a model on a diverse set of enforceable contracts and incorporate an external compliance verifier that uses rule-based checks.
Why this is correct
Fine-tuning on enforceable contracts aligns the model’s outputs with jurisdiction-specific clause patterns, while the external rule-based verifier enforces statutory constraints the model cannot guarantee. This satisfies the stem’s requirement for legally enforceable, jurisdiction-consistent clauses by combining learned drafting conventions with deterministic compliance checks, rather than relying on the model’s probabilistic recall of current law.
- ✗
Use a large instruction-tuned model with carefully engineered prompts describing jurisdiction details.
Why it's wrong here
Prompt engineering only steers the model's wording; it cannot verify that generated clauses satisfy current jurisdiction law or remain enforceable. It is tempting because prompting is fast and needs no retraining, and would suit adapting tone, format or terminology where legal correctness is not the requirement.
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