Interview Tips & Common Mistakes

After studying patterns and case studies, let's consolidate the key strategies for succeeding in AI system design interviews and mistakes to avoid.

The Interview Structure

┌────────────────────────────────────────────────────────────┐
│  Minutes 0-5: Problem Understanding                        │
│  - Listen carefully to the problem                         │
│  - Ask clarifying questions                                │
│  - Confirm scope and constraints                           │
├────────────────────────────────────────────────────────────┤
│  Minutes 5-15: High-Level Design                           │
│  - Draw system architecture                                │
│  - Identify main components                                │
│  - Discuss data flow                                       │
├────────────────────────────────────────────────────────────┤
│  Minutes 15-35: Deep Dive                                  │
│  - Detail critical components                              │
│  - Discuss trade-offs                                      │
│  - Address interviewer questions                           │
├────────────────────────────────────────────────────────────┤
│  Minutes 35-45: Production Concerns                        │
│  - Scaling strategy                                        │
│  - Monitoring and reliability                              │
│  - Cost estimation                                         │
└────────────────────────────────────────────────────────────┘

Essential Questions to Ask

Always clarify these before designing:

clarifying_questions = {
    "scale": [
        "How many requests per second?",
        "How many users?",
        "Expected growth rate?"
    ],
    "quality": [
        "What's the acceptable latency?",
        "What accuracy/precision is needed?",
        "How critical are failures?"
    ],
    "constraints": [
        "Budget for AI/LLM costs?",
        "Existing infrastructure to integrate with?",
        "Data privacy requirements?"
    ],
    "users": [
        "Who are the end users?",
        "What's their technical level?",
        "What's the primary use case?"
    ]
}

Common Mistakes to Avoid

1. Jumping to Solutions

Bad:

"We'll use GPT-4 with RAG and Pinecone..."

Good:

"Before I dive into the solution, let me understand the requirements. What's the expected query volume? What accuracy level is acceptable?"

2. Ignoring Costs

Bad:

# No cost consideration
async def process_request(query):
    response = await gpt4.complete(query)  # $0.03 per request
    return response

Good:

# Cost-aware design
async def process_request(query):
    # Try cache first ($0)
    cached = await cache.get(query)
    if cached:
        return cached

    # Use cheaper model for simple queries ($0.002)
    if is_simple_query(query):
        response = await gpt35.complete(query)
    else:
        response = await gpt4.complete(query)  # ($0.03)

    await cache.set(query, response)
    return response

3. Forgetting Failure Modes

Bad: Only describing the happy path.

Good: Proactively addressing:

failure_modes_to_discuss = {
    "llm_api_down": "Fallback to cached responses or simpler model",
    "rate_limited": "Queue requests, exponential backoff",
    "hallucination": "Fact verification layer, confidence thresholds",
    "context_overflow": "Chunking strategy, summarization",
    "cost_spike": "Budget alerts, automatic throttling"
}

4. Over-Engineering

Bad: Adding unnecessary complexity for a simple problem.

Good: Start simple, add complexity only when justified:

# Start with this
simple_design = {
    "single_llm": "gpt-3.5-turbo",
    "simple_rag": "Basic vector search",
    "no_agents": "Direct question → answer"
}

# Only upgrade if requirements demand it
complex_design = {
    "model_routing": "Only if cost is a concern",
    "multi_agent": "Only if task requires specialization",
    "hybrid_retrieval": "Only if simple RAG isn't accurate enough"
}

5. Not Discussing Trade-offs

Bad: "We'll use Pinecone for the vector database."

Good: "We have several options for the vector database..."

trade_off_discussion = {
    "pinecone": {
        "pros": ["Managed, scalable, fast"],
        "cons": ["Vendor lock-in, cost at scale"],
        "best_for": "Quick start, moderate scale"
    },
    "pgvector": {
        "pros": ["Use existing Postgres, lower cost"],
        "cons": ["Less optimized, manual scaling"],
        "best_for": "Tight budget, existing Postgres"
    },
    "qdrant": {
        "pros": ["Open source, good filtering"],
        "cons": ["Self-managed complexity"],
        "best_for": "On-prem requirements, cost sensitive"
    },
    "recommendation": "Given [requirements], I'd choose [X] because [reason]"
}

Red Flags Interviewers Look For

Red Flag	What It Signals	How to Avoid
No questions asked	Doesn't understand problem	Always clarify requirements
Hand-wavy scaling	Doesn't understand infrastructure	Give concrete numbers
Ignoring costs	Not production-ready thinking	Always estimate costs
Only happy path	Lacks operational experience	Discuss failure modes
Buzzword soup	Surface-level knowledge	Explain why each choice

Final Checklist

Before ending your interview, ensure you've covered:

final_checklist = {
    "requirements": {
        "functional": "What the system does",
        "non_functional": "Scale, latency, reliability",
        "constraints": "Budget, timeline, existing systems"
    },
    "architecture": {
        "components": "All major pieces identified",
        "data_flow": "How data moves through system",
        "apis": "Key interfaces defined"
    },
    "deep_dive": {
        "critical_path": "Most important component detailed",
        "trade_offs": "At least 2-3 discussed",
        "alternatives": "Other approaches considered"
    },
    "production": {
        "scaling": "How to handle 10x growth",
        "monitoring": "Key metrics to track",
        "failure_handling": "What happens when things break",
        "cost": "Rough estimate provided"
    }
}

Practice Approach

practice_strategy = {
    "daily": [
        "Review one system design concept",
        "Read one AI engineering blog post"
    ],
    "weekly": [
        "Complete one full mock interview",
        "Review and improve previous designs"
    ],
    "before_interview": [
        "Review RADIO framework",
        "Prepare 2-3 case studies you can reference",
        "Practice explaining trade-offs out loud"
    ]
}

Key Takeaways

1. Structure your approach - Use RADIO or similar framework
2. Ask clarifying questions - Requirements drive design
3. Discuss trade-offs - Show depth of understanding
4. Consider production - Cost, scale, monitoring, failures
5. Stay AI-specific - Don't forget LLM-specific concerns (hallucination, tokens, context limits)

Interview Tip

The best candidates:

1. Think out loud - Let the interviewer follow your reasoning
2. Draw diagrams - Visual communication is powerful
3. Acknowledge uncertainty - "I'm not sure, but I'd investigate..."
4. Ask for feedback - "Does this align with what you're looking for?"

Congratulations on completing the course! You're now ready to tackle AI system design interviews with confidence. :::