PlaybookPrompts

Design a 90-day plan for starting a new role

Productivity & Planning onboardingcareerplanning

Most 90-day plans are too generic to be useful past day 10. This prompt builds a role-specific plan with concrete milestones and the right questions to ask in each phase.

Prompt
You are a career strategist helping me build a 90-day plan for a new role. Here is my context:

Role title and level: {{ROLE_TITLE}}
Organization type and size: {{ORG_CONTEXT}}
My background (what I am bringing in): {{MY_BACKGROUND}}
What I know about why the role was created or what problem it solves: {{ROLE_CONTEXT}}
The single most important thing I need to achieve in the first 90 days per my manager: {{PRIMARY_GOAL}}

Follow these steps:

1. Split 90 days into three phases: Listen (days 1–30), Orient (days 31–60), Act (days 61–90). Write a one-sentence purpose for each phase specific to this role.
2. For each phase, write:
   - Three concrete actions I should take
   - Two questions I should get answered (name the type of person to ask)
   - One deliverable or artifact that proves I completed the phase
3. Flag the top two ways someone in this role typically fails in the first 90 days, based on what I described.
4. Identify where my background is a disadvantage in this role and suggest how to compensate.
5. Write a 30-60-90 summary I could share with my manager on day 3 to align expectations — keep it under 150 words.

Do not include generic onboarding advice that applies to every job.
Variables to fill in
  • {{ROLE_TITLE}}
  • {{ORG_CONTEXT}}
  • {{MY_BACKGROUND}}
  • {{ROLE_CONTEXT}}
  • {{PRIMARY_GOAL}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{ROLE_TITLE}}{{ORG_CONTEXT}}{{MY_BACKGROUND}}{{ROLE_CONTEXT}}{{PRIMARY_GOAL}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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