CCAO-F Prompting and Context Engineering Practice Question
A developer is building a customer support assistant using Claude. The assistant must answer questions based on a knowledge base of product manuals. The developer wants to minimize hallucinations and ensure responses are grounded in the provided documents. Which TWO strategies should the developer implement? (Choose two.)
⚠ Common exam trap
The trap here is believing that chain-of-thought prompting alone can eliminate hallucinations, when grounding in source text and explicit instructions are the key strategies.
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
✓
Include the relevant manual excerpts directly in the prompt within XML tags.
To minimize hallucinations and ground responses in provided documents, the developer should both supply the relevant excerpts in the prompt and instruct Claude to answer only from those excerpts. Including the text within XML tags focuses the model's attention, while the instruction to admit uncertainty prevents it from inventing answers. Together, these strategies create a constrained generation environment that prioritizes factual accuracy and source fidelity.
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 chain-of-thought prompt asking Claude to reason step-by-step before answering.
Why it's wrong here
Chain-of-thought can improve reasoning for complex problems, but it does not inherently prevent hallucinations when the model lacks source material. Without explicit grounding in the provided documents, step-by-step reasoning might still lead to fabricated details. It is not a primary strategy for reducing hallucinations in retrieval-based Q&A.
- ✓
Include the relevant manual excerpts directly in the prompt within XML tags.
Why this is correct
Placing relevant excerpts in the prompt gives Claude the necessary context to answer accurately. Using XML tags like <document> clearly delineates the source material, helping Claude focus on the provided text and reducing the likelihood of fabricating information. This grounding technique is highly effective for retrieval-augmented generation scenarios.
- ✓
Instruct Claude to answer only using the information provided in the documents and to say 'I don't know' if the answer is not present.
Why this is correct
Explicitly instructing Claude to rely solely on the provided documents and to admit uncertainty when the answer is absent sets a clear boundary. This reduces hallucinations by discouraging the model from drawing on its general knowledge. It also encourages honest responses, which is critical for customer support accuracy.
- ✗
Fine-tune Claude on the entire knowledge base to embed the information into the model weights.
Why it's wrong here
Fine-tuning is costly and may not effectively prevent hallucinations, as the model can still generate plausible but incorrect details. Moreover, fine-tuning does not provide a way to cite sources or ensure the model uses only the provided documents at inference time. For dynamic knowledge bases, retrieval-augmented prompting is more practical and reliable.
- ✗
Set the temperature parameter to a high value to encourage more creative responses.
Why it's wrong here
A high temperature increases randomness and creativity, which is counterproductive when the goal is factual accuracy and grounding. For support responses, lower temperatures are preferred to make outputs more deterministic and focused on the provided context. High temperature would likely increase hallucinations, not reduce them.
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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 CCAO-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 CCAO-F exam.