- A
Increase the input token limit to provide the entire contract in the prompt.
Why wrong: Long inputs can overwhelm the model and reduce focus on critical clauses.
- B
Decrease the temperature to make outputs more deterministic.
Why wrong: Deterministic does not mean relevant; it may still produce irrelevant clauses consistently.
- C
Implement Retrieval-Augmented Generation (RAG) with a curated legal clause database and a reranker to select the most on-topic passages.
RAG supplies the model with relevant context, and reranking refines the selection, directly boosting relevance.
- D
Fine-tune the base model on a labeled dataset of contract-clause pairs.
Why wrong: Fine-tuning is effective but costly and time-consuming; RAG is a lighter-weight alternative.
Question 267 of 997
Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
This Generative AI Leader practice question tests your understanding of techniques to improve generative ai model output. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A law firm uses a generative model to analyze contracts and extract key clauses. The model often outputs irrelevant clauses or misses important ones. They want to improve the relevance of the outputs without retraining the entire model. Which approach is best?
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
✓
Implement Retrieval-Augmented Generation (RAG) with a curated legal clause database and a reranker to select the most on-topic passages.
Retrieval-Augmented Generation (RAG) with a curated legal clause database and a reranker is the best approach because it grounds the model's outputs in a trusted, external knowledge base, ensuring that only the most relevant clauses are retrieved and used for generation. This directly addresses the problem of irrelevant outputs and missed clauses without requiring retraining, as the model can dynamically fetch and rank the most on-topic passages from the curated database.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the input token limit to provide the entire contract in the prompt.
Why it's wrong here
Long inputs can overwhelm the model and reduce focus on critical clauses.
- ✗
Decrease the temperature to make outputs more deterministic.
Why it's wrong here
Deterministic does not mean relevant; it may still produce irrelevant clauses consistently.
- ✓
Implement Retrieval-Augmented Generation (RAG) with a curated legal clause database and a reranker to select the most on-topic passages.
Why this is correct
RAG supplies the model with relevant context, and reranking refines the selection, directly boosting relevance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Fine-tune the base model on a labeled dataset of contract-clause pairs.
Why it's wrong here
Fine-tuning is effective but costly and time-consuming; RAG is a lighter-weight alternative.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap in Google's Generative AI exams is assuming that adjusting parameters like temperature or token limits can fix output relevance issues. While these parameters affect randomness and context length, they do not guarantee that the model will generate accurate or relevant content for domain-specific tasks. The correct approach is to use a retrieval system like RAG to ground the model in a curated knowledge base.
Detailed technical explanation
How to think about this question
RAG works by first encoding the user's query (e.g., 'extract key clauses') into a dense vector, then performing a similarity search against a vector database of pre-indexed legal clause embeddings. A reranker, often a cross-encoder model, then re-scores the top-k retrieved passages to ensure only the most semantically relevant ones are passed to the generative model, significantly reducing hallucination and improving precision. In practice, this approach allows the model to handle contracts of arbitrary length without exceeding context limits, as only the most pertinent snippets are injected into the prompt.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Techniques to Improve Generative AI Model Output — This question tests Techniques to Improve Generative AI Model Output — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement Retrieval-Augmented Generation (RAG) with a curated legal clause database and a reranker to select the most on-topic passages. — Retrieval-Augmented Generation (RAG) with a curated legal clause database and a reranker is the best approach because it grounds the model's outputs in a trusted, external knowledge base, ensuring that only the most relevant clauses are retrieved and used for generation. This directly addresses the problem of irrelevant outputs and missed clauses without requiring retraining, as the model can dynamically fetch and rank the most on-topic passages from the curated database.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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