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.
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
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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
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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