1. Fundamentals of LLMs & Generative AI
Core Concepts
Explain the transformer architecture and why it revolutionized NLP.
Detailed breakdown of attention mechanisms
Comparison with RNNs/LSTMs
Multi-head attention and positional encoding
What are the key differences between encoder-only, decoder-only, and encoder-decoder architectures?
BERT vs GPT vs T5/BART
Use cases for each architecture
Performance trade-offs
Define the following terms: pretraining, fine-tuning, instruction tuning, and RLHF.
Phase-by-phase training pipeline
Data requirements for each stage
Computational considerations
Explain how autoregressive generation works in LLMs.
Token-by-token prediction
Temperature, top-k, and top-p sampling
Beam search vs sampling
What are embeddings in the context of LLMs?
Token embeddings vs sentence embeddings
Embedding spaces and semantic relationships
Recent advances in embedding techniques
Describe the evolution from GPT-3 to GPT-4 and beyond.
Scale laws and performance improvements
Architectural innovations
Multimodal extensions
What are Mixture of Experts (MoE) models?
Sparse activation patterns
Load balancing challenges
Recent implementations (Switch Transformers, Mixtral)
Explain the concept of "emergent abilities" in LLMs.
Definition and examples
Scaling hypothesis
Controversies and debates
What is chain-of-thought prompting and why does it improve performance?
Step-by-step reasoning
Zero-shot vs few-shot CoT
Automatic CoT techniques
Define catastrophic forgetting and techniques to mitigate it.
Elastic Weight Consolidation
Gradient Episodic Memory
Continual learning approaches
2. Architecture & Model Design
Advanced Architectural Questions
Design a transformer layer from scratch. What are all the components?
Mathematical formulation
Code implementation considerations
Memory and compute optimization
Compare different attention mechanisms: full, sparse, linear, flash attention.
Computational complexity analysis
Memory efficiency trade-offs
Hardware considerations
How does rotary positional encoding (RoPE) work and why is it better than absolute positional encoding?
Mathematical formulation
Relative positional information
Length extrapolation capabilities
Explain layer normalization and why it's crucial for transformer stability.
Pre-LN vs Post-LN architectures
Gradient flow optimization
Alternative normalization techniques
What is model parallelism and how is it implemented in large model training?
Tensor parallelism
Pipeline parallelism
Expert parallelism (for MoE)
3D parallelism strategies
Describe the architecture of a vision-language model like GPT-4V or LLaVA.
Visual encoder choices (ViT, CLIP)
Projection layers and alignment
Training strategies for multimodal understanding
How do you handle extremely long contexts (1M+ tokens) in LLMs?
Sparse attention patterns
Hierarchical approaches
Recurrent memory mechanisms
Retrieval-augmented generation
Explain the concept of "activation checkpointing" and its trade-offs.
Memory vs recomputation balance
Implementation strategies
Impact on training throughput
What are state space models (SSMs) and how do they compare to transformers?
Mamba, RWKV architectures
Linear-time sequence modeling
Competitive benchmarks and limitations
Design considerations for building a multilingual LLM.
Tokenizer design for multiple languages
Balancing language distributions
Cross-lingual transfer capabilities
3. Training & Optimization
Training Pipeline Questions
Walk through the complete training pipeline for a modern LLM.
Data collection and filtering
Pretraining objectives
Supervised fine-tuning
RLHF/DPO alignment
What is the Chinchilla scaling law and how does it change model training strategy?
Optimal compute allocation
Model size vs data size trade-offs
Practical implications for training budgets
Explain mixed precision training and its benefits.
FP16, BF16, TF32 formats
Loss scaling techniques
Hardware acceleration benefits
How do you handle distributed training across thousands of GPUs?
Communication patterns
Fault tolerance strategies
Performance profiling and optimization
What is gradient checkpointing and when would you use it?
Memory reduction calculations
Performance overhead
Implementation best practices
Describe curriculum learning strategies for LLM training.
Data difficulty metrics
Scheduling algorithms
Impact on final model performance
How do you optimize batch size for large-scale training?
Gradient accumulation
Global batch size considerations
Learning rate scaling rules
Explain different optimizer choices for LLM training (Adam, AdamW, Lion, Sophia).
Memory requirements
Convergence properties
Hyperparameter sensitivity
What is model distillation and when is it useful?
Knowledge distillation techniques
Student-teacher architectures
Performance vs efficiency trade-offs
How do you prevent training instability in very large models?
Gradient clipping strategies
Learning rate warmup schedules
Weight initialization techniques
4. Prompt Engineering & Inference
Practical Application Questions
Design a prompt template for a complex task requiring multiple steps.
Role assignment
Step-by-step instructions
Output formatting requirements
Compare different inference optimization techniques: quantization, pruning, knowledge distillation.
Hardware compatibility
Accuracy-efficiency trade-offs
Real-world deployment considerations
What is speculative decoding and how does it speed up inference?
Draft model selection
Verification mechanisms
Speedup calculations
Explain the concept of "function calling" in LLMs.
Tool use capabilities
Implementation patterns
Error handling strategies
How do you implement caching for transformer inference?
KV cache mechanisms
Memory management
Cache optimization for long sequences
Design a system for few-shot learning with dynamic examples selection.
Example retrieval strategies
Similarity metrics
Performance evaluation
What are the limitations of prompt engineering?
Context window constraints
Inconsistent responses
Security vulnerabilities
Explain the concept of "toolformer" style models.
API calling capabilities
Training methodology
Integration patterns
How do you handle streaming responses in production systems?
Token-by-token generation
Client-server communication
User experience considerations
Design a system for prompt versioning and A/B testing.
Experiment tracking
Statistical significance testing
Rollout strategies
5. Evaluation & Metrics
Assessment Questions
What metrics do you use to evaluate LLM performance beyond accuracy?
Perplexity, BLEU, ROUGE
Human evaluation protocols
Task-specific metrics
How do you design a comprehensive evaluation suite for an LLM?
Benchmark selection (MMLU, HELM, BIG-bench)
Domain-specific evaluations
Safety and bias assessments
Explain the limitations of current LLM evaluation methodologies.
Benchmark contamination
Evaluation distribution shift
Cultural biases in evaluation
What is the "lost-in-the-middle" problem and how do you measure it?
Positional bias in long contexts
Evaluation strategies
Mitigation techniques
How do you evaluate reasoning capabilities in LLMs?
Mathematical reasoning benchmarks
Logical reasoning tasks
Planning and strategy evaluation
Design an experiment to measure hallucination rates.
Factual consistency metrics
Contradiction detection
Statistical significance testing
What are toxicity detection methods and their limitations?
Content moderation classifiers
Bias in moderation systems
Cultural sensitivity considerations
How do you evaluate model efficiency?
Tokens per second
Memory footprint
Energy consumption metrics
Explain the concept of "calibration" in LLMs.
Confidence scores vs accuracy
Temperature scaling
Applications in risk-sensitive domains
Design a continuous evaluation framework for a deployed LLM.
User feedback collection
Automated monitoring
Alerting and retraining triggers
6. Deployment & Production Engineering
System Design Questions
Design a scalable LLM serving architecture for millions of users.
Load balancing strategies
Model deployment patterns
Cost optimization techniques
How do you implement rate limiting and cost tracking?
Token-based billing
User quota management
Abuse detection
Explain different model serving strategies: serverless, containers, specialized hardware.
Cold start considerations
Auto-scaling policies
Cost-performance trade-offs
Design a caching system for LLM responses.
Semantic caching vs exact matching
Cache invalidation strategies
Hit rate optimization
How do you handle model versioning and rollbacks?
A/B testing infrastructure
Canary deployments
Rollback procedures
What is continuous integration for ML models?
Automated testing pipelines
Performance regression detection
Compliance checks
Design a system for monitoring LLM performance in production.
Latency tracking
Error rate monitoring
Quality metrics collection
How do you implement content moderation for user-generated prompts?
Multi-layer filtering
Real-time vs post-hoc moderation
Appeal and review processes
Explain security considerations for LLM APIs.
Prompt injection attacks
Data leakage prevention
Authentication and authorization
Design a cost-effective inference system with dynamic model selection.
Model cascade strategies
Early exit mechanisms
Quality-cost trade-off optimization
7. Ethics, Safety & Alignment
Responsible AI Questions
What are the main safety concerns with generative AI?
Misinformation generation
Bias amplification
Dual-use concerns
Explain different alignment techniques: RLHF, Constitutional AI, DPO.
Human preference modeling
Scalability challenges
Comparative effectiveness
How do you detect and mitigate bias in LLMs?
Bias measurement frameworks
Debiasing techniques
Ongoing monitoring strategies
What is the "alignment tax" and how do you minimize it?
Performance trade-offs
Optimization strategies
Evaluation approaches
Design a red-teaming strategy for LLM safety evaluation.
Adversarial prompt generation
Vulnerability assessment
Reporting and mitigation workflows
Explain the concept of "value pluralism" in AI alignment.
Cultural differences in AI preferences
Customizable alignment approaches
Implementation challenges
How do you implement content filtering without over-censorship?
Context-aware moderation
User-controlled filters
Transparency reports
What are watermarking techniques for AI-generated content?
Statistical watermarking
Detection mechanisms
Robustness against removal
Design a consent mechanism for training data usage.
Opt-out procedures
Data provenance tracking
Legal compliance frameworks
Explain the ethical considerations in AI assistants for healthcare or legal domains.
Liability frameworks
Professional standards compliance
Risk management strategies
8. Emergent Capabilities & Future Trends
Forward-Looking Questions
What are the current limitations of LLMs that you expect to be solved by 2030?
Reasoning capabilities
Context length limitations
Multimodal understanding
Explain the concept of "LLM agents" and their potential applications.
Autonomous operation capabilities
Tool use patterns
Multi-agent systems
What are neuro-symbolic approaches and their relevance to LLMs?
Combining neural and symbolic reasoning
Applications in verification and planning
Current research directions
How might LLMs evolve to handle real-time information?
Streaming data integration
World model updates
Temporal reasoning capabilities
Explain the potential of personalized AI models.
Fine-tuning on personal data
Privacy-preserving techniques
User control and transparency
What are the challenges in creating truly multilingual LLMs?
Low-resource language support
Cross-cultural understanding
Evaluation methodologies
How might LLMs integrate with traditional software systems?
API generation capabilities
Code understanding and generation
System design assistance
Explain the concept of "self-improving" AI systems.
Recursive training methodologies
Safety considerations
Control mechanisms
What are the implications of AI-generated training data?
Quality degradation concerns
Diversity preservation
Detection and filtering techniques
How do you see human-AI collaboration evolving in the next 5 years?
Interface design innovations
Trust building mechanisms
Skill augmentation patterns
9. System Design & Scalability
Technical Design Questions
Design a system that can handle 100K QPS for an LLM API.
Global load distribution
Model replication strategies
Cost optimization
How would you implement a multi-tenant LLM platform?
Resource isolation
Billing and metering
Custom model support
Design a RAG (Retrieval-Augmented Generation) system for enterprise knowledge bases.
Document processing pipeline
Vector database selection
Retrieval quality optimization
Explain how to implement continuous training for LLMs with new data.
Data pipeline design
Training job orchestration
Model version management
Design a fault-tolerant training system for 1000+ GPU clusters.
Checkpoint strategies
Failure detection and recovery
Resource utilization optimization
How do you optimize data loading for large-scale training?
Data preprocessing pipelines
Storage format optimization
Parallel loading strategies
Design a cost-monitoring system for cloud-based LLM training.
Resource tracking
Cost prediction models
Budget enforcement mechanisms
Explain GPU memory optimization techniques for inference.
Model quantization strategies
Memory sharing between requests
Dynamic batching algorithms
Design a system for A/B testing different model architectures.
Traffic splitting mechanisms
Metric collection infrastructure
Statistical analysis frameworks
How would you implement zero-downtime model updates?
Warm-up strategies
Traffic migration techniques
Rollback capabilities
10. Industry-Specific Applications
Domain Knowledge Questions
Design an LLM-based system for code generation and review.
Security consideration integration
Style guide enforcement
Testing and validation workflows
How would you apply LLMs in the healthcare domain?
HIPAA compliance considerations
Clinical decision support systems
Patient data privacy
Design a financial analysis system using LLMs.
Regulatory compliance
Real-time data integration
Risk assessment capabilities
How can LLMs transform education and tutoring?
Personalized learning paths
Assessment generation
Interactive learning experiences
Design a customer support system with LLM automation.
Escalation mechanisms
Quality assurance processes
Integration with existing CRM systems
How would you implement LLMs for creative content generation?
Style consistency maintenance
Copyright considerations
Human-in-the-loop workflows
Design a legal document analysis system.
Citation validation
Precedent analysis
Confidentiality guarantees
How can LLMs enhance scientific research?
Literature review automation
Hypothesis generation
Experimental design assistance
Design a multilingual translation system with cultural adaptation.
Cultural nuance preservation
Domain-specific terminology
Real-time adaptation capabilities
How would you build an LLM-powered personal assistant?
Context retention across sessions
Personal preference learning
Proactive assistance capabilities
Bonus: Problem-Solving & Case Studies
Scenario-Based Questions
Your LLM is generating harmful content despite safety training. How do you diagnose and fix this?
Root cause analysis methodology
Data investigation techniques
Intervention strategies
Users report that your model's performance has degraded over time. How do you investigate?
Monitoring system design
A/B testing methodology
Rollback decision criteria
You need to reduce inference costs by 50% while maintaining 95% of current quality. What's your approach?
Model optimization techniques
Hardware selection strategies
Quality-impact analysis framework
Design a system that can detect and prevent prompt injection attacks.
Attack pattern recognition
Defense-in-depth strategies
Incident response procedures
Your training job failed after 3 weeks of training on 500 GPUs. How do you recover?
Debugging methodologies
Checkpoint recovery procedures
Root cause prevention strategies
How would you design a fairness audit for an LLM used in hiring decisions?
Bias detection metrics
Demographic analysis
Mitigation implementation plans
You're asked to deploy an LLM in a regulated industry (finance/healthcare). What compliance considerations arise?
Audit trail requirements
Explainability mandates
Data governance frameworks
Design a system for real-time content moderation at scale.
Multi-stage filtering architecture
Low-latency requirements
Appeal and review workflows
How would you handle a situation where your model has memorized and is leaking sensitive training data?
Detection mechanisms
Containment procedures
Prevention strategies
Design an evaluation framework for comparing open-source vs proprietary LLMs.
Total cost of ownership analysis
Performance benchmarking
Strategic flexibility assessment
Preparation Strategies for 2026 Interviews
Technical Preparation
Hands-on Projects
Implement a small transformer from scratch
Fine-tune open-source models on custom tasks
Build and deploy a complete RAG system
System Design Practice
Whiteboard architectural diagrams
Calculate resource requirements
Design for scale and fault tolerance
Stay Current
Follow arXiv daily for latest papers
Contribute to open-source LLM projects
Attend major conferences (NeurIPS, ICML, ACL)
Interview Strategies
Communication Approach
Think aloud during problem-solving
Clarify requirements before designing
Discuss trade-offs explicitly
Knowledge Demonstration
Connect theoretical concepts to practical applications
Reference recent research appropriately
Acknowledge limitations and open problems
Problem-Solving Framework
Define success criteria first
Consider multiple approaches
Prioritize based on constraints
Resources for 2026
Books: "Deep Learning" (Goodfellow et al.), "Speech and Language Processing" (Jurafsky & Martin)
Courses: Stanford CS324, DeepLearning.ai specialization
Practice Platforms: LeetCode (system design), Hugging Face challenges
Research: Follow labs like OpenAI, Anthropic, Google DeepMind, Meta FAIR
This comprehensive list covers the breadth and depth of knowledge expected for Generative AI and LLM engineering roles in 2026. The field evolves rapidly, so staying current with the latest research and maintaining hands-on experience with cutting-edge tools and frameworks will be essential for success.