Master the 7-step framework for designing generative AI systems in technical interviews. Learn how to approach complex GenAI problems systematically, from requirements gathering to production deployment.
Why This Framework Matters
- •Structured approach to complex, open-ended GenAI problems
- •Demonstrates both technical depth and systems thinking
- •Covers unique challenges of generative AI vs traditional ML
- •Applicable to any GenAI domain: text, images, code, audio
The 7-Step GenAI Interview Framework
1. Clarify Requirements & Constraints
Define the problem scope and system boundaries
Key Questions to Address:
- ▸What type of generative AI system are we building? (text, image, audio, code, etc.)
- ▸What are the functional requirements? (generation quality, creativity, factualness)
- ▸What are the non-functional requirements? (latency, throughput, cost, safety)
- ▸Who are the users and what is the expected scale?
- ▸Are there specific domain constraints? (healthcare, finance, legal compliance)
- ▸What level of personalization is required?
Example Application:
Design a smart email composer: Generate contextually relevant email suggestions in <100ms for 300M+ users with privacy preservation
2. Frame as ML Problem
Define the ML task, inputs, outputs, and success metrics
Key Questions to Address:
- ▸What ML task type? (text generation, image synthesis, classification + generation)
- ▸What are the model inputs? (context, user profile, historical data)
- ▸What are the expected outputs? (text tokens, image pixels, embeddings)
- ▸How will we measure success? (BLEU, ROUGE, human evaluation, user engagement)
- ▸What are the failure modes and edge cases?
- ▸How do we handle multi-modal inputs/outputs?
Example Application:
Autoregressive text generation taking email context + user history → token probabilities, measured by suggestion acceptance rate >80%
3. Data Collection & Preparation
Design data pipeline for training and inference
Key Questions to Address:
- ▸What data sources are available? (user-generated content, web crawl, synthetic)
- ▸How will we handle data quality and filtering?
- ▸What are the privacy and compliance requirements?
- ▸How do we handle bias and fairness in training data?
- ▸What preprocessing steps are needed? (tokenization, normalization, augmentation)
- ▸How do we create evaluation datasets?
Example Application:
Collect 1B+ anonymized emails → privacy-preserving tokenization → bias detection → train/validation splits with differential privacy
4. Model Architecture Selection
Choose and justify the model architecture approach
Key Questions to Address:
- ▸Discriminative vs Generative model choice and rationale
- ▸Architecture selection: Transformer, CNN, RNN, VAE, GAN, Diffusion
- ▸Model size considerations: parameters vs latency vs quality trade-offs
- ▸Pre-trained vs training from scratch decisions
- ▸Multi-stage training approach (pretraining → fine-tuning → RLHF)
- ▸How to handle context length and memory constraints?
Example Application:
Decoder-only Transformer (340M params) → pretrain on web text → fine-tune on email data → optimize for <100ms inference
5. Evaluation Strategy
Define metrics, validation approach, and testing framework
Key Questions to Address:
- ▸Automated metrics: BLEU, ROUGE, perplexity, FID, CLIP score
- ▸Human evaluation: relevance, coherence, safety, helpfulness
- ▸A/B testing framework for production validation
- ▸Safety evaluation: toxicity, bias, hallucination detection
- ▸Performance benchmarks: latency, throughput, resource usage
- ▸Continuous monitoring and model drift detection
Example Application:
Combine automated metrics (BLEU >0.6) + human ratings (4.5/5 helpfulness) + A/B testing (10% acceptance rate improvement)
6. System Architecture Design
Design the end-to-end ML system architecture
Key Questions to Address:
- ▸Training infrastructure: distributed training, data parallelism, gradient accumulation
- ▸Model serving: batch vs online inference, model optimization, caching
- ▸Data flow: real-time vs batch processing, feature stores, embedding databases
- ▸Scalability: load balancing, auto-scaling, geographic distribution
- ▸Integration: APIs, SDKs, user interface components
- ▸Fallback mechanisms: graceful degradation, circuit breakers
Example Application:
TPU clusters for training → quantized models on inference servers → Redis caching → CDN distribution → browser integration
7. Deployment & Monitoring
Production deployment strategy and operational concerns
Key Questions to Address:
- ▸Deployment strategy: canary releases, blue-green deployments, gradual rollouts
- ▸Monitoring: model performance, system metrics, user engagement, safety violations
- ▸Feedback loops: user interactions → model improvements → retraining pipelines
- ▸Cost optimization: model compression, efficient serving, resource scaling
- ▸Security: model robustness, adversarial attacks, data leakage prevention
- ▸Maintenance: model updates, data drift handling, continuous learning
Example Application:
Shadow mode testing → 1% canary → gradual rollout with real-time safety monitoring + user feedback collection for model iteration
Common Interview Discussion Topics
Latency vs Quality Trade-offs
- ?How do you balance model complexity with inference speed?
- ?What techniques can reduce latency? (quantization, pruning, knowledge distillation)
- ?When would you choose a smaller, faster model over a larger, more accurate one?
- ?How do you handle real-time vs batch generation requirements?
Privacy & Data Governance
- ?How do you train models on sensitive user data while preserving privacy?
- ?What is differential privacy and when would you use it?
- ?How do you implement federated learning for personalization?
- ?What are the GDPR/data residency implications of your design?
Safety & Content Moderation
- ?How do you prevent the model from generating harmful content?
- ?What safety filters would you implement in the generation pipeline?
- ?How do you handle bias detection and mitigation?
- ?What human-in-the-loop processes are needed?
Scalability Challenges
- ?How does your system handle 10x, 100x more users?
- ?What are the computational bottlenecks in generation models?
- ?How do you optimize GPU/TPU utilization for cost efficiency?
- ?What caching strategies work best for generative models?
Practice Problems
Smart Code Completion
Design GitHub Copilot-like system for code generation
Key Requirements:
- •Multi-language support
- •<50ms latency
- •Context-aware suggestions
- •Privacy for proprietary code
Discussion Focus:
Code understanding, IDE integration, model training on code repositories, intellectual property concerns
Conversational AI Assistant
Build a customer service chatbot with personality
Key Requirements:
- •Natural conversations
- •Domain knowledge
- •Emotional intelligence
- •Escalation handling
Discussion Focus:
Dialog state management, knowledge grounding, personality consistency, human handoff
Creative Content Generator
Instagram-style image generator with text prompts
Key Requirements:
- •High-quality images
- •Style transfer
- •Content safety
- •Copyright compliance
Discussion Focus:
Diffusion models vs GANs, prompt engineering, NSFW detection, attribution systems
Personalized News Summarizer
Generate personalized news summaries for millions of users
Key Requirements:
- •Factual accuracy
- •Personalization
- •Real-time updates
- •Multi-source aggregation
Discussion Focus:
Fact-checking mechanisms, bias in summarization, user preference learning, source credibility
Tips for GenAI Interview Success
Do's
- ✓Start with clarifying questions - GenAI problems are often ambiguous
- ✓Discuss trade-offs explicitly - latency vs quality, cost vs performance
- ✓Address safety and ethics proactively - critical for GenAI systems
- ✓Think about the full product experience, not just the model
Don'ts
- ✗Jump straight into model architecture without understanding requirements
- ✗Ignore data privacy and compliance considerations
- ✗Propose overly complex solutions without justification
- ✗Forget about production concerns like monitoring and maintenance