CCAO-F Using the Claude API Practice Question
A developer is implementing a retrieval-augmented generation pipeline with the Claude Messages API. Retrieved documents are inserted into the user turn, and the developer wants the model to cite which document supports each claim. Which technique most directly improves the model's ability to attribute statements to specific source documents?
⚠ Common exam trap
The trap here is treating attribution as a generation-quality problem solvable with temperature or token limits, when it is really a prompt-structure problem that requires explicit document identifiers.
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
✓
Number or tag each retrieved document in the prompt and instruct the model to reference those identifiers in its answer.
Attribution improves when the prompt makes source boundaries explicit. Labeling each retrieved document with a stable identifier and instructing the model to cite that identifier gives it a concrete token to attach to each claim, and gives the application something to validate. Sampling settings and output length do not create source-to-claim mappings, and removing document separators actively destroys the structure needed for citation.
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 max_tokens so the model has room to quote each source document verbatim in the answer.
Why it's wrong here
A larger output budget lets the model write more, but it does not improve its ability to map claims to sources. Verbatim quoting also bloats responses and can still be misattributed if documents are unlabeled. The attribution problem is about prompt structure and identifiers, not about how many tokens the model may generate.
- ✓
Number or tag each retrieved document in the prompt and instruct the model to reference those identifiers in its answer.
Why this is correct
Giving each document a stable identifier and asking the model to cite it creates an explicit mapping between claims and sources. The model can then emit something like a document number or tag alongside each statement, which the application can verify against the retrieved set. This structured labeling is the most direct way to improve attribution accuracy in a RAG prompt.
- ✗
Concatenate all retrieved documents into one continuous block with no separators to keep the prompt compact.
Why it's wrong here
Merging documents into one undifferentiated block removes the boundaries the model needs to attribute a claim to a particular source. Without separators or identifiers, the model cannot reliably tell where one document ends and another begins, so citations become guesswork. Compactness is not worth losing the structural cues that make attribution possible.
- ✗
Raise the temperature so the model explores more of the retrieved context before answering.
Why it's wrong here
Higher temperature increases sampling randomness, which makes attribution less consistent rather than more. It does not give the model any structural cue about which document a claim came from, and it can encourage invented citations. For an attribution task, determinism and explicit document labeling matter far more than exploratory sampling, so this choice works against the stated goal.
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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
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