Prompting and Task Execution
Free Claude Certified Associate - Foundations practice — 6 questions on Prompting and Task Execution, with explanations. No sign-up. Full 12-question mixed test →
A knowledge worker needs Claude to analyze a 50-page legal contract and produce a structured risk summary with citations to specific clauses, so a lawyer can quickly verify each finding against the source text. Which approach BEST ensures accuracy and traceability?
Requiring Claude to ground each risk in a direct quote, structured with XML tags, forces verifiable grounding to source text and lets the reviewer check each claim against the exact clause, directly reducing unverifiable summarization.
A team needs Claude to classify 500 customer support tickets into one of 5 fixed categories with perfectly consistent output formatting for downstream parsing. Which prompting technique is MOST effective for this requirement?
Multishot prompting with concrete labeled examples anchors Claude to the exact desired input/output format and category boundaries, which is the most reliable way to achieve consistent, parseable classification at scale.
You need Claude's response to start directly with raw JSON output, with no preamble like 'Here is the JSON you requested:'. Which technique reliably achieves this?
Prefilling the start of the Assistant turn (e.g., with '{') causes Claude to continue generation from that point, skipping any conversational preamble and reliably producing output that starts exactly where the prefill left off.
A user wants Claude to produce a 20-page technical report requiring research synthesis, outline creation, section drafting, and final editing. Which task decomposition strategy is MOST effective for achieving high-quality output?
Decomposing a large, multi-stage task into sequential steps with checkpoints (outline approval, section-by-section drafting, dedicated editing) lets the user catch errors early, keeps each prompt focused, and produces materially higher quality than a single monolithic request.
What is the primary functional difference between placing instructions in the system prompt versus embedding them in the user turn when prompting Claude?
The system prompt is designed to establish persistent context, role, and behavior guidelines that persist for the duration of the interaction, whereas user turn content is the specific, per-turn request Claude is asked to act on.
A Claude-generated draft is factually correct but uses a tone that's far too casual for the target executive audience. What is the MOST effective way to iterate on this output?
Effective iteration requires specific, actionable feedback — naming the exact problem (tone), showing a concrete example of the desired result, and pointing to the affected content — which gives Claude the information needed to make a targeted correction while preserving the already-correct factual content.
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