GitHub Copilot Prompt to Create a Loyalty Program
GitHub Copilot is an AI-powered development assistant that excels at generating structured and functional code. To create a loyalty program, Copilot becomes a valuable ally: it understands complex business logic like point calculations, reward tiers, expiration rules, and customer engagement mechanics. By formulating a precise prompt, you get a complete architecture covering the data model, APIs, point accumulation and redemption logic, and administrative interfaces. Whether you're developing a tiered program for e-commerce, a cashback system for a mobile app, or a referral program for a marketplace, GitHub Copilot generates production-ready code following best practices for your tech stack. The main challenge is to clearly specify business rules — point conversion rates, reward unlock conditions, member status management — so that the generated code is directly usable. This guide offers an optimized prompt and its variants for creating a robust, scalable loyalty program tailored to your specific needs.
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Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
Create a complete loyalty program with the following specifications:
Data Model:
- Table
members: id, user_id, tier (bronze/silver/gold/platinum), total_points, lifetime_points, joined_at, tier_expires_at - Table
transactions: id, member_id, type (earn/redeem/expire/adjust), points, source (purchase/referral/bonus/review), reference_id, created_at - Table
rewards: id, name, description, points_cost, tier_required, stock, is_active - Table
tier_rules: id, tier_name, min_points, multiplier, benefits_json
Business Rules:
- Point earning: €1 spent = 10 points, bonus x1.5 for Silver, x2 for Gold, x3 for Platinum
- Tiers: Bronze (0-999), Silver (1000-4999), Gold (5000-14999), Platinum (15000+)
- Points expire after 12 months of inactivity
- Tier reassessment every quarter based on lifetime_points of the last 12 months
- A member cannot drop more than one tier per quarter
REST API to implement:
- POST /members/enroll: enroll in the program
- POST /members/{id}/earn: credit points after a purchase
- POST /members/{id}/redeem: exchange points for a reward
- GET /members/{id}/balance: balance, tier, recent history
- GET /members/{id}/rewards: available rewards based on tier and balance
- POST /admin/tier-recalculation: quarterly reassessment job
Stack: TypeScript, Node.js, PostgreSQL with Drizzle ORM. Include validations, error handling, unit tests for point calculations and tier transitions.
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Why this prompt works
This prompt is effective because it explicitly defines the data schema, quantified business rules, and expected endpoints, leaving no ambiguity for Copilot about the target architecture. Specifying multipliers per tier, thresholds, and time constraints forces the generation of complete business logic rather than a generic skeleton. By stating the tech stack and expected deliverables (validations, errors, tests), the prompt sets the quality bar for the generated code.
Use Cases
Variants
Expected Output
You get a functional loyalty system with the complete Drizzle ORM schema, business services for point calculation with multipliers, automatic tier management and expiration, and documented REST API routes. The code includes unit tests covering critical scenarios: tier upgrades and downgrades, point expiration, attempts to redeem with insufficient balance.
Frequently Asked Questions
How does GitHub Copilot handle complex tier and multiplier logic?
GitHub Copilot excels at generating conditional logic when rules are explicitly defined in the prompt. By specifying point thresholds for each tier (Bronze 0-999, Silver 1000-4999, etc.) and associated multipliers, Copilot generates accurate calculation functions with the appropriate conditions. The trick is to always provide exact numeric values rather than vague descriptions. If the generated logic is incorrect, add concrete examples in the prompt: "A Gold member who spends €50 should receive 1000 points (50 × 10 × 2)".
Should you generate the loyalty program all at once or in multiple steps?
For optimal results, proceed in multiple steps with Copilot. Start with the database schema and TypeScript types, then ask for business services (point calculation, tier management), followed by API endpoints, and finally tests. This incremental approach allows Copilot to leverage the context of already generated code in the file and produces more coherent code. Each step can be refined before moving on to the next, avoiding the need to refactor a monolithic block.
How can you ensure the code generated by Copilot properly handles point expiration and edge cases?
Explicitly include edge cases in your prompt: what happens if a member tries to redeem more points than they have, if points expire during a transaction, or if a member exactly reaches a tier threshold. Also, ask for unit tests targeting these scenarios. Copilot then generates safeguards like balance checks before redemption, atomic transactions to prevent inconsistencies, and cleanup jobs for expiration. Always double-check the generated date logic — this is where Copilot makes the most errors.
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Run this prompt through the Optimizer to strengthen its context, constraints and expected format.
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- LéaAI
Pour des réponses plus fiables, découpez ce prompt : demandez d'abord le schéma Drizzle (tables/relations), puis chaque endpoint séparément, et enfin les tests. Ajoutez des exemples de payloads JSON pour guider les validations et les codes d'erreur. Copilot gère mieux les sous-tâches que les demandes globales.
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