Skip to main content
Beautiful AI Prompt
Back to Prompts Directory
Job InterviewSystem DesignadvancedFeatured

Senior System Design Mock Interviewer & Grader

Conduct a realistic, challenging interactive system design interview with detailed rubrics.

#system-design#interviews#distributed-systems#scalability#architecture

Compatibility & Specs

Compatible AI Models
ClaudeChatGPT
Last UpdatedMar 3, 2026
Customizable Variables3 parameters
Main Prompt Template

138 words • 1011 characters

You are a Principal Infrastructure Architect and Interview Bar Raiser at a premier technology company. You will conduct a comprehensive System Design interview with me. System to Design: [system_to_design] Expected Scale: [scale_requirements] Specific Focus: [focus_area] Your instructions: 1. Start by challenging me with the exact scenario, establishing constraints, and asking me how I would approach Functional and Non-Functional Requirements. 2. Do NOT give away the answer upfront. Simulate a real collaborative interview turn-by-turn. 3. Push me on database partitioning strategies, caching invalidation pitfalls, consistency vs. availability trade-offs (CAP theorem), and single points of failure (SPOFs). 4. After I present my design, provide a rigorous Evaluation Scorecard across 4 dimensions: (A) Requirement Scoping, (B) High-Level Architecture, (C) Deep Dive & Trade-offs, and (D) Communication. Begin the interview now by setting the problem constraints and asking your first framing question.
Tip:Variables marked in purple brackets can be tailored below.

Customize Prompt

Fill in the variables below. Your customized prompt updates instantly in the browser — no AI API needed.

Parameters (3)* Indicates required field
[system_to_design]

The target distributed architecture to be designed

[scale_requirements]

Traffic, storage, or concurrency parameters

[focus_area]

Technical trade-offs to stress test during the mock

3 parameters active
Live Customized OutputLive Preview
168 words
You are a Principal Infrastructure Architect and Interview Bar Raiser at a premier technology company. You will conduct a comprehensive System Design interview with me. System to Design: Design a Distributed Rate Limiter for a Public API Gateway Expected Scale: 100k requests/second across 10 global edge regions with sub-5ms latency penalty Specific Focus: Trade-offs between centralized Redis vs local in-memory token buckets with eventual consistency Your instructions: 1. Start by challenging me with the exact scenario, establishing constraints, and asking me how I would approach Functional and Non-Functional Requirements. 2. Do NOT give away the answer upfront. Simulate a real collaborative interview turn-by-turn. 3. Push me on database partitioning strategies, caching invalidation pitfalls, consistency vs. availability trade-offs (CAP theorem), and single points of failure (SPOFs). 4. After I present my design, provide a rigorous Evaluation Scorecard across 4 dimensions: (A) Requirement Scoping, (B) High-Level Architecture, (C) Deep Dive & Trade-offs, and (D) Communication. Begin the interview now by setting the problem constraints and asking your first framing question.
Launch in:
ChatGPTClaudeGemini

How to Use This Prompt

Follow this 3-step workflow to extract high-signal responses from any compatible AI model.

01

1. Tailor the Parameters

Use the interactive customizer above to substitute the bracketed placeholders with your exact context, requirements, and constraints.

02

2. Send to AI Model

Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot. These models follow structured multi-step constraints reliably.

03

3. Review and Iterate

Review the output against the verified benchmark below. Follow up in the conversation to stress-test edge cases or refine tone.

Prompt Variables & Parameters

Reference breakdown of every dynamic variable embedded in this prompt template.

PlaceholderParameter NameTypeStatusDescription & Guidance
[system_to_design]System to DesigntextRequiredThe target distributed architecture to be designedDefault: Design a Distributed Rate Limiter for a Public API Gateway
[scale_requirements]Scale RequirementstextRequiredTraffic, storage, or concurrency parametersDefault: 100k requests/second across 10 global edge regions with sub-5ms latency penalty
[focus_area]Specific FocustextareaOptionalTechnical trade-offs to stress test during the mockDefault: Trade-offs between centralized Redis vs local in-memory token buckets with eventual consistency

Example Execution & Benchmark Output

Sample input arguments and the verified AI response demonstrating expected quality and formatting.

Sample VariablesInputs
[system_to_design]
Design a Distributed Rate Limiter for a Public API Gateway
[scale_requirements]
100k requests/second across 10 global edge regions with sub-5ms latency penalty
[focus_area]
Trade-offs between centralized Redis vs local in-memory token buckets
Expected AI ResponseVerified Result
Welcome to your System Design Interview. Today, we need you to design a **Distributed Rate Limiter** to protect our core API infrastructure. **Here are your baseline constraints:** - **Scale**: We process 100,000 requests per second across 10 geographic edge points of presence (PoPs). - **Performance**: Rate limit checks must execute in < 5ms. - **Failure Mode**: Graceful degradation if the rate limiter subsystem experiences a partition. To kick off: Walk me through your **functional requirements**, **non-functional requirements**, and what rate-limiting algorithm you'd select (e.g. Token Bucket, Leaky Bucket, Sliding Window Log) and why.

Best Use Cases

Scenarios and roles where this prompt produces maximum leverage.

Software engineers preparing for Staff and Senior FAANG/Tier-1 interviews
Architects evaluating distributed system trade-offs before implementation
Tech leads sharpening technical evaluation and interview skills

Tips for Best Results

Techniques to elevate response fidelity

  • •Anchor your inputs with concrete metrics (revenue influenced, latency reduced, team size) rather than generic qualitative claims.
  • •Paste the exact requirements and keywords from your target job description to match recruiter ATS filters and interview rubrics.
  • •Ask the model to generate 2-3 variations with differing executive tones (e.g., visionary leader vs. hands-on technical operator).

Common Mistakes to Avoid

Frequent failure modes and anti-patterns

  • •Allowing the model to fabricate achievements or metrics that you cannot defend during in-depth technical loops.
  • •Leaving variable brackets unfilled, which results in obvious template placeholders reaching hiring managers.
  • •Using passive job descriptions (e.g. 'assisted with') instead of quantified leadership actions.

Part of Curated Collections

This prompt is sequenced as part of these goal-oriented workflows

View all collections
Job Interviews
8 Prompts

Ace Your Job Interview

Simulate realistic interview loops with strict grading rubrics across behavioral STAR stories, technical design, and reverse questions.

#Interview Prep#STAR Method+3
Explore Collection

Related AI Prompts

Complementary workflows in Job Interview

View all Job Interview prompts
Codingadvanced

Principal Code Reviewer & Architecture Auditor

Conduct rigorous architectural code reviews identifying memory leaks, race conditions, and typing holes.

claudechatgptcopilot
#code-review#clean-code#architecture
View Prompt
Job Interviewbeginner

Behavioral STAR Interview Story Polisher

Turn messy work stories into crisp 90-second Situation-Task-Action-Result interview answers.

claudechatgptgemini
#interview#behavioral-questions#star-method
View Prompt
Resumebeginner

ATS-Optimized Resume Bullet Point Transformer

Turn passive job duties into high-impact Google XYZ formula achievement bullets.

chatgptclaudegeminiperplexitycopilot
#resume#ats#impact-metrics
View Prompt

Related Engineering Guides

Deep-dive playbooks and system prompt methodologies for Job Interview

View all guides
Prompt Engineering
8 min read

How to Write Better AI Prompts

A comprehensive playbook for crafting high-fidelity prompts: mastering context, roles, objectives, constraints, output schemas, few-shot examples, and systematic iteration.

Mar 25, 2026Read Guide
Interview Preparation
8 min read

AI Prompts for Job Interviews

Master high-stakes behavioral and technical interviews using AI as an interactive sparring partner: STAR framework structuring, mock panel simulations, and real-time critique.

Mar 28, 2026Read Guide