Capstone: Full System Design Pack

This is where everything comes together.

Up to now, you’ve learned individual pieces of system design: databases, caching, queues, AI systems, payments, observability, and real-world tradeoffs. In real interviews—and real jobs—you are never asked to design just one piece. You are asked to design a complete product and explain it clearly.

This final capstone is designed to simulate exactly that experience.

You will choose one product and design it end-to-end, as if you were in a senior system design interview or writing a production-ready design doc for your team.

Step 1: Choose one product (don’t try to do all)

Pick one of the following. Choosing well matters more than choosing hard.

You can design an AI Tutor (perfect for DevsCall-style platforms), a Marketplace (buyers, sellers, payments), a Social App (feeds, chat, notifications), or a Monitoring Tool (metrics, alerts, dashboards).

The key rule: pick a product you can explain confidently. Interviewers reward clarity, not ambition.

Once chosen, stick with it for the entire capstone. Do not change scope halfway.

Step 2: Requirements document (your foundation)

Start with a short but clear requirements document.

Functional requirements describe what the system must do. For example, “Users can ask questions to an AI tutor” or “Users can upload and watch videos.”

Non-functional requirements describe how the system should behave. Latency targets, availability expectations, scalability assumptions, and security constraints belong here.

This document sets boundaries. Every design decision later should trace back to one of these requirements. If you can’t explain why a component exists, it probably doesn’t belong.

In interviews, this step shows maturity. Skipping it is one of the biggest red flags.

Step 3: Capacity estimation (rough, not perfect)

Now estimate scale.

How many users? How many daily active users? How many requests per second at peak? How much data stored per day or per year?

You are not expected to be exact. You are expected to be reasonable and explicit about assumptions.

For example, “Assume 100k DAU, 10 actions per user per day, peak traffic is 5× average.” This is enough.

Capacity estimation drives choices: cache or no cache, sharding or not, async or sync. Without it, design decisions are guesswork.

Step 4: High-Level Design (HLD)

This is the diagram interviewers care about most.

Your HLD should show clients, APIs, core services, databases, caches, queues, and external systems. It should show request flow, not just boxes.

Keep it simple. Fewer boxes explained well beat complex diagrams explained poorly.

As you explain the HLD, always answer two questions:
What happens on the happy path?
What happens when something fails?

If you can answer both, your design feels real.

Step 5: API specification and database schema

Now zoom in.

Define key APIs with inputs and outputs. You don’t need every endpoint—only the ones that define the system’s behavior.

Then define the core database schema. Focus on entities, relationships, and indexes that support your main query patterns.

This step proves you can translate architecture into concrete implementation details, which interviewers value highly.

Step 6: Scaling and reliability plan

Now stress the system.

What happens when traffic doubles? Which components scale horizontally? Where do you add caches? Where do you add queues?

Then discuss failures. Cache down, DB slow, queue backlog, external service outage. For each, explain how the system degrades gracefully.

This is where you connect lessons on replication, retries, timeouts, circuit breakers, and fallbacks.

A strong answer shows that failure is expected, not exceptional.

Step 7: AI integration plan (if applicable)

If your product includes AI, explain where AI fits and where it does not.

Describe the AI pipeline: ingestion, retrieval (RAG if used), inference, and output. Explain latency-sensitive vs async usage. Explain cost controls.

Most importantly, explain guardrails. How do you prevent hallucinations? How do you cite sources? What happens if the model fails?

This step distinguishes AI-aware system designers from people who just “add GPT.”

Step 8: The interview narrative (the hidden deliverable)

This is the most important part—and the one most people skip.

Your interview narrative is how you would explain this system in 30–45 minutes, step by step, on a whiteboard.

You should be able to say:

• Here’s the problem and scope
• Here are my assumptions
• Here’s the high-level architecture
• Here’s how data flows
• Here’s how it scales
• Here’s how it fails safely
• Here’s where AI fits (if applicable)

Practice telling this story out loud. If you can explain it calmly and clearly, you are interview-ready.

Final thoughts

This capstone is not about perfection. It’s about confidence, clarity, and tradeoffs.

A strong system design candidate doesn’t know every tool. They know how to reason, how to communicate, and how to justify decisions.

If you can complete this capstone honestly—without copying templates—and explain it end to end, you are already operating at a senior level.

This is not the end of learning. But it is the point where everything finally clicks.