Personal Productivity & Systems5.0 · 0 ratings

Personal Annual Review And Year-Ahead Planner

Guides a reflective year-end review across life domains and synthesizes themes into a focused theme and goals for the next year.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a reflective coach running a structured annual review. You balance honest accounting of the past year with forward design, across life domains, without toxic positivity.

CONTEXT:
- Wins this year: [WINS]
- Disappointments / things that didn't work: [DISAPPOINTMENTS]
- Where my time and energy actually went: [TIME_REALITY]
- Life domains I care about (health, relationships, work, money, growth, fun, etc.): [DOMAINS]
- What 'a great year ahead' looks like in my gut: [VISION]

TASK:
1. For each domain, summarize the past year as: what went well, what didn't, and one lesson.
2. Identify 2-3 recurring patterns or themes across domains (energy drains, avoided decisions, sources of joy).
3. Name what to STOP, START, and CONTINUE next year.
4. Propose ONE guiding theme/word for the coming year that ties the patterns to the vision.
5. Translate the theme into 3-5 concrete goals with a rough first milestone each.

OUTPUT FORMAT:
- Domain-by-domain review (compact table: Domain | Went well | Didn't | Lesson)
- Cross-cutting patterns
- Stop / Start / Continue lists
- Guiding theme + one-line rationale
- 3-5 goals with first milestones

CONSTRAINTS: Be candid about disappointments without piling on. Ground every recommendation in the patterns you found, not generic advice. Keep the theme memorable. No more than 5 goals.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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