CCAR-F Context and Reliability Practice Question
An application uses Claude 3.5 Sonnet to summarize legal documents. Occasionally, the model hallucinates clauses not present in the source text. What is the most effective architectural approach to ground the model's output?
⚠ Common exam trap
Candidates often assume that fine-tuning the base model or lowering the temperature will eliminate hallucinations, missing the necessity of external data grounding.
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 to inject the relevant document snippets into the system prompt and instruct the model to only use that data.
Implementing Retrieval-Augmented Generation (RAG) forces the model to rely on provided documents rather than internal training weights. By injecting the specific legal clauses into the system prompt context, you create a rigid boundary for the model's knowledge. This architectural pattern is essential for high-stakes domains like legal or medical analysis, where accuracy is non-negotiable and hallucinations can lead to significant liability or incorrect decision-making.
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 temperature setting to 1.0 to ensure the model explores more creative possibilities.
Why it's wrong here
Increasing the temperature encourages randomness and diversity in responses. In a legal context requiring strict adherence to source material, higher temperature settings actually exacerbate hallucination risks by allowing the model to diverge from the provided context in favor of higher-probability tokens that are not grounded in the source text.
- ✗
Fine-tune the model on the full legal corpus to embed the documents directly into its weights.
Why it's wrong here
Fine-tuning is excellent for style, tone, and domain-specific vocabulary, but it is a poor mechanism for factual grounding. Weights are static and cannot be updated easily when documents change. Furthermore, fine-tuning does not guarantee that the model will distinguish between learned patterns and hallucinated facts during inference.
- ✓
Use RAG to inject the relevant document snippets into the system prompt and instruct the model to only use that data.
Why this is correct
RAG provides the specific, authoritative source material directly within the context window for every request. By instructing the model to rely exclusively on this provided context and return 'I don't know' if the information is missing, you effectively minimize the model's propensity to generate ungrounded, hallucinated legal clauses.
- ✗
Reduce the maximum tokens allowed for the response to prevent the model from generating extra text.
Why it's wrong here
While limiting output length might cut off a hallucination, it does not address the root cause of the error. The model may still produce incorrect, hallucinated information within the truncated response. This is a crude mitigation strategy that compromises utility without actually solving the underlying grounding problem effectively.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Anthropic exam blueprint
This CCAR-F practice question is part of Courseiva's free Anthropic certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the CCAR-F exam.