Overview
This prompt aims to guide developers in creating a Daily Reward system for user engagement. Programmers and software developers will benefit from the structured approach and detailed requirements provided.
Prompt Overview
Purpose: This system aims to encourage user engagement through a structured daily login reward mechanism.
Audience: It is designed for developers looking to implement user retention strategies in applications.
Distinctive Feature: The system uniquely tracks login streaks while considering time zones and daylight savings changes.
Outcome: Users receive daily rewards, fostering consistent engagement and interaction with the application.
Quick Specs
- Media: Text
- Use case: Generation
- Industry: Banking (Retail & Commercial), Development Tools & DevOps, Productivity & Workflow
- Techniques: Decomposition, Self-Consistency, Structured Output
- Models: Claude 3.5 Sonnet, Gemini 2.0 Flash, GPT-4o, Llama 3.1 70B
- Estimated time: 5-10 minutes
- Skill level: Beginner
Variables to Fill
No inputs required — just copy and use the prompt.
Example Variables Block
No example values needed for this prompt.
The Prompt
Create an advanced Daily Reward system for a user login interface with the following features:
– The system tracks a daily login streak.
– Each day the user logs in consecutively within 24 hours, the daily reward progresses to the next one.
– If the user misses logging in for more than 24 hours, the streak breaks and resets back to day 1.
– The rewards cycle within a week (7 days); after this period, the reward list resets and continues the cycle.
– The system should clearly indicate:
– Current day in the streak
– Time remaining before the next reset
– Reward earned each day
– It must handle time zones correctly to ensure streak consistency.
# Steps
1. Track user login times and determine if each login is within 24 hours of the previous one.
2. Increment the streak if the login is within 24 hours; otherwise, reset the streak.
3. Assign rewards based on the current streak day (1 through 7), then reset to 1 after day 7.
4. Display streak status and reward information to the user clearly.
# Output Format
Provide the detailed logic and pseudocode or code snippets demonstrating how to implement this Daily Reward system, including:
– Handling of streak resets
– Weekly reward resets
– User notifications
# Notes
– Ensure that time comparisons consider user local time zones.
– Consider edge cases such as:
– Daylight savings changes
– Irregular login patterns
# Examples
– Day 1 login:
– streak = 1
– reward = first reward in cycle.
– Day 2 login within 24 hours:
– streak = 2
– reward = second reward.
– Missed day 3 (more than 24 hours):
– streak resets to 1 on next login.
– After day 7, the next login resets to reward cycle day 1 again.
Screenshot Examples
How to Use This Prompt
- Copy the prompt for the Daily Reward system.
- Identify the key features and requirements listed.
- Outline the steps for implementation based on the provided logic.
- Write pseudocode or code snippets for each step.
- Test the system for edge cases and time zone handling.
- Document the process and results for future reference.
Tips for Best Results
- Track Logins: Implement a function to log user login times and check if each login is within 24 hours of the last.
- Increment or Reset Streak: Create logic to increment the streak if the login is timely; otherwise, reset the streak to 1.
- Assign Rewards: Use a simple array to assign rewards based on the current streak day, resetting to day 1 after day 7.
- Display Status: Ensure the user interface shows the current streak day, time remaining until the next reset, and the reward for that day clearly.
FAQ
- What does the Daily Reward system track?
It tracks a user's daily login streak and rewards earned. - What happens if a user misses a login for 24 hours?
The streak resets back to day 1 if the user misses logging in. - How long is the reward cycle in the system?
The reward cycle lasts for 7 days before resetting. - How does the system handle time zones?
It ensures streak consistency by considering user local time zones.
Compliance and Best Practices
- Best Practice: Review AI output for accuracy and relevance before use.
- Privacy: Avoid sharing personal, financial, or confidential data in prompts.
- Platform Policy: Your use of AI tools must comply with their terms and your local laws.
Revision History
- Version 1.0 (February 2026): Initial release.
