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Using AI Reflection Prompts: “Your Year with ChatGPT” as a Tool to Prepare for 2026

  • Writer: Amanda Grutza
    Amanda Grutza
  • Jan 4
  • 5 min read

Updated: Jan 8

The LLM AI site ChatGPT year in wrapped screen showing "Your Year with ChatGPT" prompting the user to begin their wrapped

By Amanda Grutza

Thoughts on using technology with intention


Overview


This doc explains how to use a reflection prompt alongside a personal “Year Wrapped” (such as a summary generated from LLM usage) to identify patterns, clarify tradeoffs, and plan next steps in a structured way.

The Idea Behind the AI Reflection Prompts


The goal is to use a “Year Wrapped” as input for reflection, rather than as an evaluation.

Instead of asking:

  • How do I fix my problems?

  • What should I optimize next?

  • What am I doing wrong?


The prompt focuses on:

  • What patterns are already present

  • What those patterns provide

  • What they cost

  • What small structural changes may help


How to Use the Prompt

There are multiple ways to use this prompt, depending on preference:

  • Run it once with an LLM and annotate the output.

  • Copy the structure into a document or notebook and answer it manually.

  • Work through one section per month rather than completing it all at once.

  • Treat it as a living document rather than a one-time exercise.

There is no required cadence.

A user interacting with the AI model ChatGPT and requesting an overview of their strengths and friction.

A Simple Version of the Prompt

This shorter version is intended for quick reflection, journaling, or a single conversation.

Simple Reflection Prompt

I have reviewed a summary of my past year and want help reflecting on it. Based on what you know about my patterns, strengths, and challenges: What strengths were most evident this year? Where did I experience the most friction or energy drain? What tradeoffs am I currently operating within? What small, realistic adjustments could make the next year more sustainable or aligned? Please avoid generic advice or motivational language. I am looking for clarity, not optimization.

The Full Universal A.I. Reflection and Growth Prompt

This version is more detailed and structured. It can be used with an LLM, adapted into a document or PDF, or used as a workbook. It can also be incorporated into a personal tracking system.

UNIVERSAL REFLECTION AND GROWTH PROMPT

(Post–Year Wrapped Deep Dive)

Context

I have reviewed a personal “Year Wrapped” or annual summary reflecting my patterns, habits, interests, strengths, challenges, and how I spent my time and energy over the past year.

I want to use this information as a basis for intentional reflection and adjustment over the next year.

I am not seeking motivational content or generic self-improvement advice. I want a grounded analysis that reflects my actual patterns and working style.

Request

Based on the information available from my past year, please do the following:

1. Strengths Profile

Identify my core strengths as they currently appear in practice.

For each strength:

  • Describe how it shows up in my daily work or thinking

  • Explain why it is useful

  • Note how it could become limiting if overused or unbalanced

2. Growth Edges and Friction Points

Identify meaningful areas for growth, framed as:

  • Patterns that create friction

  • Habits that consume energy or reduce clarity

  • Tendencies that limit sustainability

For each area:

  • Describe how it appears in real situations

  • Explain the tradeoff involved

  • Avoid judgmental or prescriptive language

3. Personal Style and Work Patterns

Summarize my working style, including:

  • How I process information

  • How I make decisions

  • How I manage complexity

  • Which environments or rhythms support my work

  • What tends to cause overload or fatigue

4. Targeted Growth Strategies

For each growth area, suggest practical strategies that:

  • Align with my existing habits

  • Do not require a fundamental change in personality

  • Emphasize structure or small design adjustments rather than willpower

5. 12-Month Guided Reflection Path

Outline a flexible framework for the next year that includes:

  • Broad phases or themes

  • What to focus on first, and why

  • How reflection and action can reinforce one another

  • How to periodically pause and reassess

This framework should be adaptable across areas such as career, learning, creativity, relationships, health, or balance.

6. Format

Present the output in a clean structure that can be:

  • Copied into a document or plain Notion page

  • Printed and annotated

  • Revisited over time

Avoid tool-specific formatting or aesthetic language. Use clear, plain language.

7. Tone

  • Neutral and calm

  • No motivational clichés

  • No leadership framing unless directly relevant

  • Respect for personal autonomy

End Goal

The output should function as:

  • A summary of the past year

  • A reference point for the next one

  • A document that can be revisited and updated over time

    Output from the AI LLM ChatGPT showcasing customized strategies based on the users personal habits to help them plan for building in 2026.

How Effective Can an LLM-Based Reflection Be?

When This Works Well

The effectiveness of this prompt depends on how an LLM has been used over time and the amount of interaction data available.

A “Year Wrapped” generated from LLM usage reflects interaction patterns, not personal identity.


These patterns may include:

  • Types of questions asked

  • How problems are framed

  • Recurring topics

  • Requests for structure, creativity, reassurance, or synthesis

  • Frequency of iteration and revision

  • Where conversations stop or shift

The prompt treats these patterns as behavioral data rather than labels.

Core Use Cases

1. Pattern Recognition

For users who have used an LLM as a thinking aid, drafting space, or problem-framing tool, interaction history can reveal:

  • Approaches to complexity

  • Recurring points of friction

  • Topics that consistently draw attention

  • Cognitive energy patterns

The prompt helps organize this information into a usable summary.

2. Incremental Adjustment

This prompt is useful for people seeking adjustment rather than reinvention. It supports review and recalibration during periods of transition or reassessment.

3. Translating Insight Into Structure

The longer prompt follows a sequence:

Observation → interpretation → tradeoffs → structural adjustments

This is suited to users who prefer analysis and design changes over habit-based or motivational approaches.

How Much LLM Usage Is Sufficient?

There is no fixed threshold, but usefulness increases with signal quality.

High Usage

Typically includes:

  • Weekly or more frequent use

  • Repeated engagement with similar topics or projects

  • Requests for revision or deeper analysis

  • Use of the LLM as a thinking aid

In these cases, patterns are more likely to be clear.

Moderate Usage

Includes:

  • Periodic but consistent use

  • Focused use during specific projects or periods

  • Use primarily for writing, planning, or learning

Insights are likely directional rather than comprehensive.

Low Usage

Includes:

  • Infrequent or one-off use

  • Primarily factual queries

  • Little iteration or follow-up

In these cases, the prompt may function better as a self-guided reflection tool rather than relying on AI-generated summaries.

What This Tool Is Not Designed For

This prompt is not intended for:

  • Mental health assessment

  • Relationship analysis

  • Standalone major life decisions

  • Productivity scoring or comparison

It should be treated as input, not authority.

Responsible Use

  • Treat outputs as hypotheses

  • Note agreement and disagreement

  • Observe emotional responses as well as cognitive ones

  • Revisit over time rather than acting immediately

  • Unexpected reactions can also be informative!

Closing Note

This prompt is most effective when used as:

  • A reflection aid

  • A pattern summary

  • A starting point for ongoing adjustment

Its value depends less on volume of LLM usage and more on how thoughtfully the output is reviewed and applied.


 
 
 

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