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Directness

As explained in Prompt Engineering in Practice

Directness is a prompt-expression dimension that determines whether instructions are stated explicitly or conveyed indirectly.

Directness controls whether a prompt states its instruction explicitly or conveys it indirectly. It concerns instruction style, not an aggressive tone. Precision is a separate control: it determines how specifically the prompt states its requirements. section 3.2

Choose the instruction style to match the job:

  • Use implicit framing to invite options and considerations in a conversational response. section 3.2
  • Use explicit instructions when the response must reach a decision and identify ownership or next actions. section 3.2

Flaky CI test strategy

An execution-oriented prompt asks for a primary approach to reducing flaky CI tests this sprint. It directs the model to evaluate test isolation, retry-limit reduction, and parallelism tuning, then return a final decision, one key risk, and the first rollout step. Those requested fields make it possible to check whether the answer is actionable rather than merely advisory. section 3.2.2

Directness is a practical control, not a guarantee: implicit prompts can yield actions, and explicit prompts can express uncertainty. In reusable planning templates, pair clear commands with inputs and an output format so decisions remain concrete and reviewable. section 3.2.2 section 5.2.1

How Prompt Engineering in Practice covers it

How deep it goes
Defines implicit and explicit instruction styles, contrasts their typical responses, and cautions that the effects are not deterministic.
Examples
Uses a flaky CI test decision and contrasting CI/CD architecture prompts to show instruction styles in engineering tasks.
What you can do after
Practice/perform prompt revision by choosing exploratory framing or explicitly requesting a decision, risk, and rollout step.
How it connects
Distinguishes directness from precision and pairs it with brevity, prompt templates, and output formats.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Prompt Engineering in Practice. The section links point to the parts of the book it draws on.