Briefing Mindset
The habit of prompting Claude the way you would brief a brilliant new colleague: highly capable, and entirely without context on your company, project and standards until you supply it. Everything a good delegation contains, a good prompt contains.
Exam context: The foundation of the whole domain. Keyed answers supply the context a new colleague would need; distractors assume Claude will figure it out.
See also: 1.1 Creating Effective Prompts for Business Tasks
Five Components of an Effective Prompt
The five parts of a complete business prompt: the task (a verb plus a deliverable), the context Claude cannot know, the actual source material, the output specification, and an example when standards matter.
Exam context: When an item compares prompts, count the components. The keyed answer is almost always the option that supplies the missing one; distractors polish wording while the real gap stays open.
See also: 1.1 Creating Effective Prompts for Business Tasks
Output Specification
The part of a prompt that states the deliverable's format, length, tone and structure: three bullet points per workstream, neutral tone, under 400 words.
Exam context: State length as a number, not as "short". A prompt with no output specification leaves format and length to chance, and the rework bill to you.
See also: 1.1 Creating Effective Prompts for Business Tasks
Role Framing
Opening a prompt by asking Claude to respond from a named role, such as "as an HR communications specialist". A role shapes vocabulary, tone and priorities, and even a single sentence makes a difference.
Exam context: Paired with audience naming, which changes the output more than most instruction tweaks: the same analysis briefed for the board and for the project team should read differently.
See also: 1.1 Creating Effective Prompts for Business Tasks
Source Material
The actual inputs a task needs: the report, the meeting notes, the data, pasted into the conversation or uploaded as files. Claude works from what it is given.
Exam context: Describing a document from memory instead of attaching it is a recurring distractor; the from-memory version adds errors and omissions the real document does not have.
See also: 1.1 Creating Effective Prompts for Business Tasks
Task Decomposition
Structuring a complex, multi-part request into pieces Claude can do properly, one at a time, instead of cramming it into a single prompt. The official remedy list for an unhelpful answer includes breaking complex requests into substeps.
Exam context: The tested judgement is matching technique to task: connected parts want one staged conversation, independent deliverables want separate chats, unclear structure wants a plan first.
See also: 1.2 Decomposing Complex Requests
Numbered Steps
The lightest decomposition technique: listing a task's connected parts as an explicit sequence inside one prompt. Claude follows numbered steps far more reliably than a run-on paragraph of asks.
Exam context: The right choice when order or completeness matters but the work still fits one prompt; escalating past it when it would do is mild over-engineering.
See also: 1.2 Decomposing Complex Requests
Staged Prompting
Working through a layered deliverable stage by stage in one conversation: outline first, review and adjust, then draft section by section. Each stage's approved output becomes the foundation for the next.
Exam context: The keyed answer for connected work that must share one voice. Course corrections at the outline stage cost seconds; the same corrections after 20 pages do not.
See also: 1.2 Decomposing Complex Requests
Plan-First Prompting
Asking Claude to propose an approach before executing: "before writing anything, list the sections you would include and what each covers". Reviewing a plan takes seconds; reworking a wrong 2,000-word draft does not.
Exam context: Earns its place only when the right structure is genuinely unclear. Offered where the structure is already known and agreed, it is a small over-engineering detour the exam plants as a tempting refinement.
See also: 1.2 Decomposing Complex Requests
Mega-Prompt
The anti-pattern of cramming an entire multi-part deliverable into one prompt and asking for the finished product in a single pass. Attention spreads unevenly, so some parts come back strong and others skimmed.
Exam context: One of the two decomposition failure modes planted as distractors; the other is fragmenting connected work across separate chats. Both are plausible second-bests to the staged conversation.
See also: 1.2 Decomposing Complex Requests
Memory (claude.ai)
Claude's cross-chat memory, built as a set of individual topics covering your role, projects, preferences and ongoing work context. It arrived on 11 September 2025 for Team and Enterprise plans, reached Pro and Max from 23 October 2025, and as of 30 September 2026 is on by default for Free, Pro and Max plans, while on Team and Enterprise plans an owner decides whether memory is available, and it stays off for each member until that member turns it on.
Exam context: Memory is context about you, saved as individual topics, not transcripts or working documents. It does not carry an approved outline or tone guide into a new chat; explicit carry-forward remains your job.
See also: 1.2 Decomposing Complex Requests
Chat Search
The ability, on paid plans, to ask Claude to search past conversations and reference relevant details. It is retrieval you must prompt for, not automatic carry-over.
Exam context: Distractors treat chat search or memory as automatic transport for working material between chats; the keyed answer pastes the output in, re-attaches the file, or keeps it in a Project.
See also: 1.2 Decomposing Complex Requests
Iteration
The normal working rhythm with Claude: output, specific feedback, better output. First outputs are drafts, and the professionals who get the most from Claude budget a round or two of feedback into every task.
Exam context: Iteration is not a failure state. The exam contrasts it with the two losing postures: accepting a mediocre first draft, and abandoning the tool after one attempt.
See also: 1.3 Iterating Prompts to Improve Output
Targeted Feedback
Follow-up that names the gap between the output and the need: the audience, the tone, the length, the scope, or the missing constraint. "Cut it to 200 words, keep the pricing paragraph, lose the marketing adjectives" is targeted; "make it better" is not.
Exam context: The tested skill of objective 1.3. The vague retry reproduces the problem because no new information enters the conversation; the keyed answer always names the fix.
See also: 1.3 Iterating Prompts to Improve Output
Re-Briefing
Rewriting the original prompt and starting clean when the output reveals the brief itself was wrong: wrong task, wrong framing, or missing context. Patching a wrong brief across many turns fights the framing instead of fixing it.
Exam context: The miscall runs in both directions: patching a fundamentally wrong brief turn after turn, and restarting when the output was one specific fix away. Items reward reading which situation the stem describes.
See also: 1.3 Iterating Prompts to Improve Output
Research
The claude.ai feature for multi-source questions: Claude works agentically, running multiple searches that build on each other and delivering a report with easy-to-check citations. Ordinary web search suits quick factual queries answered in one or two lookups.
Exam context: Brief Research like a research question, not a topic: scope, angle, and what the findings must inform. Research for a quick fact is over-engineering; a single search for a multi-source comparison is under-powering.
See also: 1.4 Adapting Strategy to Task Type
Diverge-Then-Converge
The brainstorming sequence: open the funnel first by asking for quantity and range with judgement withheld, then converge in a second step by applying criteria, shortlisting and developing the survivors.
Exam context: Constraints applied at the divergence step strangle the variety that is the entire point; criteria belong in the convergence step. The tightly constrained brainstorm is a classic crossed-wires distractor.
See also: 1.4 Adapting Strategy to Task Type