Domain 1: Prompting and Task Execution
14% of examWrite effective, well-structured prompts
Key Points
- Give Claude what/why/for whom/in what form -- brief it like a capable new colleague, not a search box.
- Six reliable building blocks: task/goal, context, audience & tone, format, constraints, examples.
- Positive instructions ('write in plain language') beat long lists of prohibitions; put the key instruction near the actual task, not buried in a preamble.
- Reusing a well-received past output as an example is one of the most effective levers for steering tone and format.
- Separate instructions from pasted source data with a heading like 'Source document:' so the model doesn't read data as a command.
- Claude only knows the prompt, attached Project knowledge, and its training data -- never your organization's unstated context.
Decision Rules
When: Output feels generic despite a reasonably clear goal
→Diagnose missing audience, tone, format, or constraint elements before regenerating.
When: A prompt pastes a transcript, policy, or spreadsheet alongside an instruction
→Label the source material (e.g., 'Source document:') so it can't be mistaken for a command.
When: You're tempted to write a long list of prohibitions
→Rewrite as positive instructions instead of stacking 'don'ts.'
When: The question implies a shorter prompt is inherently better
→Reject that framing -- specificity, not brevity, drives quality; under-specification is the usual failure.
✗ Anti-Patterns to Reject
- Treating 'shorter is always better' as a rule -- vague, under-specified prompts are the most common cause of weak output.
- Assuming Claude already knows organizational acronyms, policies, or product context it was never given.
- Pasting instructions and source data together with no delimiter between them.
Decompose complex requests into checkable steps
Key Points
- Decomposition breaks a fuzzy or complex goal into ordered, checkable steps instead of one large ask.
- Sequential (prompt chaining): complete and review step 1 before feeding it into step 2.
- Structured single prompt: spell out ordered sub-steps within one prompt for moderately complex tasks that don't need a human check between steps.
- Decomposition is what makes intermediate verification possible -- check each piece instead of judging one giant, undifferentiated output.
- Mirrors AI Fluency's Delegation and Description: hand off well-scoped pieces, framed clearly.
- Decomposition is about structure and order, not word count -- a long but unstructured prompt is not decomposed.
Decision Rules
When: A task has ordered, dependent sub-steps (extract, then group, then recommend)
→Use sequential prompt chaining, reviewing each step before the next.
When: The task is moderately complex but doesn't need a human check between sub-steps
→Use a structured single prompt that spells out the ordered sub-steps.
When: Faced with a vague, sprawling request like 'what should we do?' over 50 raw notes
→Decompose into extract -> group -> recommend rather than asking for everything at once.
✗ Anti-Patterns to Reject
- Solving a multi-part problem in a single vague prompt when the pieces should be sequenced and checked.
- Confusing decomposition with just making the prompt longer -- length is not structure.
Iterate deliberately to improve Claude's output
Key Points
- Iteration means diagnosing the specific gap (format, detail, tone, factual error, length), then making one targeted change.
- The loop: read the output against your intent -> make one targeted change -> re-run and compare -> keep or discard.
- Change exactly one variable per iteration so you can attribute the result to that specific edit.
- Mirrors AI Fluency's Diligence competency: you stay responsible for judging and refining the result.
- Regenerating without changing the prompt is not iteration -- it's random re-rolling.
Decision Rules
When: The first draft misses the mark
→Name the exact gap (format/detail/tone/fact/length) before changing anything.
When: You're deciding how many things to change in one pass
→Change exactly one variable so you can tell what helped or hurt.
When: A regenerate with no prompt change still didn't fix the output
→Recognize that regeneration alone isn't iteration -- diagnose and edit instead.
✗ Anti-Patterns to Reject
- Repeatedly hitting 'regenerate' without changing the prompt.
- Changing five things at once (tone, format, length, constraints), so no single edit can be credited or blamed.
Adapt prompting strategy to the type of task
Key Points
- Analysis: supply the source data, state the lens, ask for reasoning before conclusions.
- Research: request structured findings with verifiable, traceable sources and explicit scope/recency.
- Drafting: specify audience, tone, length, and format, plus a style example.
- Brainstorming: invite quantity and range ('10 varied ideas, including unconventional ones'); defer filtering to a later step.
- Reasoning-before-conclusions improves quality on analysis/multi-step tasks; it's unnecessary overhead for simple lookups or short drafts.
- Confident-sounding research output is not the same as verified output -- sourcing must still be checked.
Decision Rules
When: The task is analysis or multi-step reasoning
→Ask Claude to reason through the evidence before giving the final answer.
When: The task is a simple lookup or short draft
→Skip reasoning-first overhead -- it adds length without added benefit.
When: The task is brainstorming
→Invite quantity and range and defer filtering, rather than asking for 'the one best idea.'
When: The task is research
→Require structured findings with traceable citations and explicit scope/recency, not just a confident narrative.
✗ Anti-Patterns to Reject
- Using one rigid prompt template for every task type -- a brainstorm prompt should not look like a compliance-analysis prompt.
- Treating a fluent, well-organized research answer as trustworthy without verifiable sourcing.