IVQA 51-100
51. What is layer normalization and why is it important in Transformers?
Layer normalization normalizes the inputs across the features (channels) for each token, ensuring stable and consistent distributions throughout the network. ✅ It is crucial in Transformers to improve convergence, stabilize training, and reduce internal covariate shift, especially in deep architectures.
52. How does beam search differ from greedy decoding and top-k sampling?
Greedy decoding picks the highest probability token at each step.
Top-k sampling randomly samples from the top k probable tokens.
Beam search maintains k best partial sequences (beams) at each step and expands them, selecting the top k combinations. ✅ Beam search explores more combinations and often yields better quality text than greedy, but is more deterministic than top-k sampling.
53. What is temperature in GenAI models, and how does it affect output?
Temperature controls the randomness in sampling:
Low temperature (< 1): sharper, more confident predictions (less diversity).
High temperature (> 1): flatter probabilities, more randomness (more creativity). ✅ It's a tunable parameter to balance between determinism and creativity.
54. What is causal masking and where is it used?
Causal masking ensures each token only attends to previous tokens (not future ones). ✅ Used in decoder-only models (like GPT) during both training and inference to maintain autoregressive behavior.
55. Explain attention heads. Why use multiple heads?
Each attention head learns a different projection of queries, keys, and values. ✅ Multiple heads allow the model to attend to information from different representation subspaces in parallel, enhancing context understanding.
56. How do you prevent exposure bias during training?
Exposure bias arises because models are trained with ground-truth tokens but must generate sequences during inference. ✅ Mitigation techniques:
Scheduled sampling (mix of predicted and ground-truth tokens during training)
Reinforcement learning (optimize for sequence-level objectives)
Professor forcing or diffusion-based models
57. What is the difference between training and inference time masking?
Training masking: includes techniques like causal masks, padding masks, and sometimes random masking for tasks like MLM.
Inference masking: focuses on ensuring the model does not peek at future tokens (causal) and handles variable-length inputs (padding).
✅ Masking strategies must adapt to task (MLM vs CLM) and model (encoder vs decoder).
58. What are the trade-offs between depth and width in transformer models?
Depth (layers): better abstraction and understanding of complex patterns, but can cause vanishing gradients and higher latency.
Width (hidden size, heads): richer representations per layer, but more memory and compute costs.
✅ Trade-off depends on target task: depth suits longer dependencies, width helps with feature richness.
59. Why do larger models sometimes perform worse than smaller fine-tuned ones?
Overfitting, lack of task-specific knowledge, or distribution shift can cause poor generalization.
Smaller fine-tuned models are optimized for specific tasks, leading to better performance on that domain.
✅ Bigger isn’t always better without fine-tuning or domain alignment.
60. How does dropout help prevent overfitting in GenAI models?
Dropout randomly disables a portion of neurons during training, forcing the model to learn redundant and robust representations. ✅ It regularizes the model, reduces reliance on specific weights, and improves generalization.
61. What is gradient checkpointing and why is it used in training large LLMs?
Gradient checkpointing saves memory by not storing intermediate activations during forward pass. Instead, it recomputes them during the backward pass. This allows training large models with limited GPU memory at the cost of additional computation.
62. How would you train a model like GPT-2 on a custom dataset?
Tokenize the dataset using GPT-2's tokenizer.
Format the dataset into input sequences with proper attention masks.
Use Hugging Face Transformers or PyTorch with the GPT-2 architecture.
Fine-tune using causal language modeling loss (CrossEntropyLoss).
Use distributed training, mixed precision, and checkpointing for efficiency.
63. Explain the concept of data curriculum in model training.
A data curriculum involves presenting training data in a meaningful order—typically from easy to difficult—so the model learns foundational patterns before complex ones. It improves convergence speed, stability, and sometimes final accuracy.
64. What are some techniques to reduce hallucination during training?
Improve data quality and reduce noisy examples.
Apply retrieval-augmented generation (RAG).
Introduce supervised fine-tuning (SFT) with ground-truth outputs.
Use reward models in reinforcement learning (e.g., RLHF).
Penalize factual errors using custom loss functions.
65. How do you select hyperparameters for a GenAI model?
Use a combination of:
Prior knowledge (e.g., common learning rates, batch sizes).
Grid/random search or Bayesian optimization.
Monitor metrics like perplexity or BLEU on validation set.
Tools like Optuna or Weights & Biases for systematic tuning.
66. What role does batch size play in model convergence?
Large batch size: faster training, stable gradients, but may generalize worse.
Small batch size: better generalization, more noisy updates.
Must balance based on GPU capacity and task; can use gradient accumulation to simulate larger batches.
67. What is zero-shot vs. few-shot vs. fine-tuning?
Zero-shot: Use the model directly without examples.
Few-shot: Provide a few examples in the prompt to guide the model.
Fine-tuning: Adjust model weights with task-specific data for better performance.
68. Describe how mixed precision training works.
Mixed precision uses both FP16 and FP32 during training to speed up computation and reduce memory usage. FP32 is retained for critical operations (like loss scaling) to maintain stability, while most calculations are done in FP16.
69. What is weight decay and how does it affect large model training?
Weight decay adds a penalty to large weights during optimization (L2 regularization). It helps prevent overfitting and encourages simpler models, which is crucial for generalization in large models.
70. How can transfer learning benefit GenAI development?
Transfer learning allows starting from pre-trained models (like GPT, BERT) and adapting them to new tasks with fewer data and training resources. It speeds up development and improves performance on domain-specific tasks.
71. How do you evaluate creativity in GenAI outputs?
Creativity is subjective, so it's usually assessed through human evaluation, using metrics like novelty, fluency, coherence, and surprise. Some frameworks also use diversity and originality scores, but there’s no universal automated metric.
72. What is BLEU score and when is it used?
BLEU (Bilingual Evaluation Understudy) measures the overlap between generated text and reference text using n-gram precision. It’s widely used in machine translation but doesn’t account for synonyms or fluency.
73. Compare BLEU, ROUGE, METEOR, and BERTScore.
BLEU
n-gram precision
Fast, standard for MT
Ignores recall, no semantics
ROUGE
n-gram recall
Good for summarization
Ignores synonyms
METEOR
Precision + recall + synonyms
Handles word variations
Slower than BLEU/ROUGE
BERTScore
Semantic similarity using BERT
Captures meaning, context-aware
Computationally expensive
74. What is perplexity, and what does it tell you about a language model?
Perplexity measures how confident a model is in predicting the next token. Lower perplexity means better language modeling. However, it doesn’t directly correlate with human-perceived quality for text generation.
75. How do you evaluate factual correctness in LLM-generated answers?
Human fact-checking
Automated tools (e.g., retrieval-augmented verification)
Knowledge-grounded QA datasets
Factuality metrics like FEVER, or LLM-based consistency checks (e.g., self-ask or chain-of-verification).
76. What are human-in-the-loop evaluation methods?
These involve real users or annotators reviewing and scoring model outputs for quality, relevance, accuracy, and tone. It’s essential for subjective or task-specific feedback (e.g., RLHF).
77. How do you A/B test different prompts or models in production?
Randomly assign users to variant A or B
Measure KPIs like click-through rate, satisfaction, completion rate
Use statistical significance tests (e.g., t-test)
Collect both quantitative metrics and qualitative feedback
78. What’s the importance of response diversity in GenAI?
High diversity avoids repetitive or generic responses and improves engagement, especially in creative tasks like story writing or dialogue. But too much diversity may reduce coherence or relevance.
79. What is the Turing Test, and how does it apply to modern LLMs?
The Turing Test evaluates if a machine can imitate human behavior well enough to fool a human judge. While some LLMs can pass simple versions of it, passing the test doesn’t imply true understanding or reasoning.
80. What are the limitations of automated evaluation metrics in GenAI?
Don’t capture creativity, intent, or nuance
May reward generic over insightful answers
Fail to detect hallucinations or factual errors
Can be gamed or optimized blindly, hurting real-world performance
81. Design a GenAI system that generates marketing content at scale.
Input: Product specs, brand tone, campaign type
Pipeline:
Content template library
Prompt tuning or fine-tuning with marketing data
LLM inference (OpenAI/GPT, Claude, etc.)
Post-processing (SEO enrichment, grammar check)
Scale: Use batch generation + asynchronous queues (e.g., Celery, Prefect)
Storage: Save outputs in CMS or asset management systems
Feedback: Track engagement metrics for continuous improvement
82. How would you build a secure GenAI-powered HR assistant?
Authentication: Role-based access control (RBAC) with audit logging
Data Handling: Mask PII in prompts; use encrypted storage
LLM: RAG-based approach with a restricted HR knowledge base
Privacy: Filter and redact sensitive outputs
Deployment: Private cloud or on-prem LLM if needed (e.g., Azure OpenAI or LLaMA2 self-hosted)
83. What architecture would you use for a GenAI-powered code assistant?
Frontend: VSCode plugin or web IDE
Backend: FastAPI server with caching
LLM Integration: OpenAI Codex, Claude, or fine-tuned CodeLlama
Context Handling: Chunked file inputs + vector search for relevant snippets
Feedback Loop: Capture corrections or manual edits for retraining
84. How do you add memory or context persistence to GenAI agents?
Use a memory backend like Redis, PostgreSQL, or ChromaDB
Store previous interactions as structured events or embeddings
Inject relevant memory context into future prompts
Implement summarization or retrieval mechanisms for long-term memory
85. What’s the role of a vector store in a GenAI-powered search app?
A vector store (e.g., Qdrant, Weaviate, Pinecone) stores embedding vectors of documents or metadata. It enables semantic search by finding conceptually similar items based on user queries, enriching RAG or search-based applications.
86. How do you handle GenAI latency and scale in a production API?
Asynchronous APIs with FastAPI or Node.js
Prompt caching for frequent queries
Batching requests via queue (e.g., Kafka, Celery)
Use OpenAI functions or multi-threaded inference if on local models
Horizontal scaling via Kubernetes or serverless functions
87. How do you version and monitor GenAI models in production?
Model versioning: Use DVC or MLflow to track model artifacts
Prompt versioning: Tag prompts via Git or prompt management tools
Monitoring: Log latency, token usage, hallucination rates, user feedback
Integrate alerts for anomalies or performance degradation
88. How would you implement feedback loops for improving GenAI output?
Collect user thumbs up/down, corrections, or edited responses
Use LLM to classify feedback into categories (e.g., hallucination, off-topic)
Retrain or fine-tune with this feedback data periodically
Optional: RLHF pipelines with ranking models
89. What are the data pipelines needed for a custom GenAI chatbot?
Data Collection: Ingest FAQs, docs, chat logs
Preprocessing: Clean, chunk, and embed content
Storage: Save raw + embedded data in a vector DB
RAG Pipeline: Retrieve relevant context → generate answer via LLM
Monitoring: Track usage and feedback for refinement
90. How do you optimize cost while using OpenAI APIs at scale?
Use smaller models (e.g.,
gpt-3.5-turbo) where possibleCache frequent prompts/responses
Truncate context intelligently to reduce token usage
Switch to open-source models for less sensitive or offline tasks
Monitor token spend with tools like Langfuse, Helicone, or OpenAI dashboards
91. What are token-efficient architectures and why do they matter?
Token-efficient architectures reduce the number of tokens required for processing or generating outputs. This lowers latency, compute cost, and token limits—especially important when working with large context windows. Examples include long-context transformers (like Mamba, FlashAttention, RWKV) and models with sparse attention or compression techniques.
92. How do mixture-of-experts (MoE) models help with GenAI scaling?
MoE models activate only a subset of model parameters per inference, reducing computation while maintaining high capacity. This enables larger models with manageable resource usage. Examples include GLaM, Switch Transformer, and DeepSpeed-MoE.
93. What is speculative decoding in LLMs?
Speculative decoding uses a small, fast model to generate draft tokens and then verifies or revises them using a larger, more accurate model. This speeds up generation while preserving output quality. It’s a way to optimize inference time in production.
94. How is diffusion being used for multimodal GenAI tasks?
Diffusion models (like Stable Diffusion, DALL·E 3) are used to generate high-quality images, videos, and audio from text prompts. They are now being extended to multimodal reasoning, where vision-language models generate content conditioned on both text and images.
95. What are some promising open-source GenAI projects today?
LLaMA 3 (Meta) – strong open alternative to GPT
Mistral – lightweight performant models
Ollama – simple local LLM deployment
LangChain / LlamaIndex – agent & RAG frameworks
AutoGen / CrewAI – multi-agent orchestration
LMQL – prompt programming with logic control
96. How can GenAI be used for real-time data analytics?
Natural Language Queries on dashboards
Auto-generated summaries of streaming data (e.g., logs, social media)
Anomaly detection + explanation
Integrated with tools like Apache Kafka or dbt for live reporting via GenAI frontends.
97. What is the role of synthetic data in GenAI pipeline bootstrapping?
Synthetic data helps in:
Pretraining or fine-tuning when real data is scarce
Bias mitigation via controlled generation
Testing edge cases in model evaluation It accelerates model development while reducing privacy risks.
98. How do emerging standards like OpenGPTs or Model Catalogs help teams?
They enable:
Interoperability between GenAI tools and platforms
Clear model capabilities, limitations, and versioning
Discovery and reuse of models across orgs This fosters safer, compliant, and collaborative AI development.
99. What are some unexplored opportunities in GenAI for enterprise SaaS?
Auto-generation of reports from business data
Contract redlining and negotiation agents
Context-aware onboarding assistants
Embedded GenAI in CRM/ERP systems
AI agents for regulatory compliance tracking
100. What research direction in GenAI excites you the most right now?
The intersection of agentic AI workflows (AutoGPT, CrewAI) with persistent memory and planning, enabling autonomous multi-step tasks with tools. Also, multimodal models that reason across text, image, and code hold great promise.
Would you like these combined into a forward-looking Substack article or GenAI trend repor
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