1Z0-1127-25 LLM Fundamentals Practice Question
An organization wants to deploy an LLM for legal document analysis where accuracy is critical, and the model must not reference any external data outside the provided legal corpus. Which approach BEST satisfies these requirements?
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
✓
Use RAG with a vector store containing only the legal documents, and set the retriever to return a fixed number of chunks with high similarity threshold
RAG can ground generation in a curated corpus, and with strict retrieval settings (e.g., only retrieving from the legal corpus), the model will not use any outside knowledge, reducing hallucinations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a decoder-only model with zero-shot prompting
Why it's wrong here
Zero-shot prompting relies on the model's internal knowledge, which may include information outside the legal corpus.
- ✗
Use a fine-tuned encoder-only model for classification only
Why it's wrong here
Classification only provides labels, not the detailed analysis required.
- ✗
Use a large foundation model with a high temperature setting
Why it's wrong here
High temperature increases randomness, which is risky for accuracy; the model may still generate information not in the corpus.
- ✓
Use RAG with a vector store containing only the legal documents, and set the retriever to return a fixed number of chunks with high similarity threshold
Why this is correct
RAG ensures answers are grounded in the provided legal corpus; similarity threshold can prevent retrieval of irrelevant chunks.
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