13 September 2025

#LLM

#LLM

Key Concepts


Topic Sub-Topics Basic Intermediate Advanced Expert
Introduction to LLMs What are LLMs, Evolution (GPT, BERT, LLaMA, etc.), Applications ✅ ✅ ❌ ❌
Foundations of NLP Tokenization, Embeddings, Bag of Words, Word2Vec, Transformers ✅ ✅ ✅ ❌
Architecture Transformer architecture, Attention mechanism, Encoder vs Decoder models ❌ ✅ ✅ ✅
Training Fundamentals Pretraining, Fine-tuning, Self-supervised learning, Transfer learning ❌ ✅ ✅ ✅
Optimization & Scaling Gradient descent, Optimizers (Adam, RMSProp), Scaling laws, Mixed precision training ❌ ❌ ✅ ✅
Datasets & Preprocessing Text cleaning, Tokenization strategies (BPE, SentencePiece), Data augmentation ✅ ✅ ✅ ❌
Evaluation Metrics Perplexity, BLEU, ROUGE, Accuracy, Human eval, Bias & toxicity tests ✅ ✅ ✅ ✅
Prompt Engineering Zero-shot, Few-shot, Chain-of-thought, Instruction tuning ❌ ✅ ✅ ✅
Fine-Tuning Techniques Full fine-tuning, LoRA, Prefix tuning, Parameter-efficient methods ❌ ❌ ✅ ✅
Knowledge & Memory Context windows, Retrieval-Augmented Generation (RAG), Vector databases ❌ ❌ ✅ ✅
Model Deployment API serving, Model quantization, ONNX/TensorRT, Edge deployment ❌ ✅ ✅ ✅
Scaling Infrastructure Distributed training, Model parallelism, Pipeline parallelism, GPUs vs TPUs ❌ ❌ ✅ ✅
Ethics & Safety Bias, Fairness, Hallucinations, Responsible AI, Red-teaming ✅ ✅ ✅ ✅
Security Concerns Prompt injection, Data leakage, Model stealing, Adversarial attacks ❌ ❌ ✅ ✅
Open-Source vs Proprietary LLMs Hugging Face models, LLaMA, Falcon, vs GPT, Claude, Gemini ✅ ✅ ✅ ✅
Multimodal LLMs Text-to-Image (Stable Diffusion), Text-to-Speech, Vision-Language models ❌ ❌ ✅ ✅
Applications Chatbots, Code generation, Search, Healthcare, Finance ✅ ✅ ✅ ✅
Advanced Topics RLHF (Reinforcement Learning with Human Feedback), Constitutional AI, MoE (Mixture of Experts) ❌ ❌ ✅ ✅
Future Trends AGI, Continual learning, Energy efficiency, Neuromorphic LLMs ❌ ❌ ❌ ✅

Interview question

1. Fundamentals of LLMs

  1. What is a Large Language Model?
  2. How do LLMs differ from traditional NLP models?
  3. Why did transformers replace RNNs and LSTMs?
  4. What is self-supervised learning in LLMs?
  5. Explain tokenization and its importance.
  6. What are embeddings in NLP?
  7. Explain positional encoding in transformers.
  8. What is the difference between BERT and GPT?
  9. What is a context window?
  10. What are hallucinations in LLMs?
  11. What are scaling laws for LLMs?
  12. What is transfer learning in LLMs?
  13. Why are LLMs considered ?foundation models??
  14. What is the difference between open-source and proprietary LLMs?
  15. What are the common applications of LLMs?

2. Transformer Architecture

  1. Explain the transformer architecture.
  2. What is self-attention?
  3. What is multi-head attention and why is it used?
  4. Explain feed-forward networks in transformers.
  5. Why do transformers use residual connections?
  6. How does layer normalization help?
  7. What is causal masking in transformers?
  8. What is cross-attention?
  9. Encoder-only vs decoder-only vs encoder-decoder models.
  10. Why are transformers parallelizable?
  11. How does positional encoding support sequence modeling?
  12. What is the difference between GPT-3 and GPT-4 architectures?
  13. How does memory usage grow in transformers?
  14. What are sparsity-based transformer variants?
  15. What is the role of attention weights interpretability?

3. Training & Optimization

  1. What is masked language modeling?
  2. What is causal language modeling?
  3. What is curriculum learning in LLMs?
  4. What datasets are used in pretraining?
  5. What is catastrophic forgetting?
  6. Explain fine-tuning vs pretraining.
  7. What is RLHF (Reinforcement Learning with Human Feedback)?
  8. How does PPO work in RLHF?
  9. What is gradient checkpointing?
  10. Explain mixed precision training.
  11. What is pipeline parallelism?
  12. What is model parallelism?
  13. How do you avoid exploding gradients?
  14. Why is batch size important in training?
  15. What are hyperparameters that affect convergence?

4. Prompt Engineering

  1. What is prompt engineering?
  2. Zero-shot vs few-shot prompting.
  3. What is chain-of-thought prompting?
  4. What is instruction tuning?
  5. Explain in-context learning.
  6. What is prompt injection?
  7. What are system prompts?
  8. Explain role of examples in few-shot prompting.
  9. What is contextual priming?
  10. What are negative prompts?
  11. How to mitigate hallucination via prompts?
  12. Explain chain-of-verification prompting.
  13. What is multi-turn prompting?
  14. How does temperature affect responses?
  15. How does top-k vs nucleus sampling affect outputs?

5. Fine-Tuning & Adaptation

  1. Full fine-tuning vs parameter-efficient tuning.
  2. What is LoRA (Low-Rank Adaptation)?
  3. What is prefix-tuning?
  4. What are adapter layers?
  5. Why is fine-tuning expensive?
  6. What is multi-task fine-tuning?
  7. What is instruction fine-tuning?
  8. Explain domain adaptation in LLMs.
  9. What is continual learning?
  10. What are challenges in domain-specific LLMs?
  11. What is RAG (retrieval-augmented generation)?
  12. What is knowledge distillation for LLMs?
  13. What is model quantization?
  14. Explain distillation vs pruning.
  15. What is Constitutional AI?

6. Evaluation & Metrics

  1. What is perplexity?
  2. What is BLEU score?
  3. Difference between BLEU, ROUGE, METEOR.
  4. Why accuracy isn?t enough for LLMs?
  5. Explain human evaluation in LLMs.
  6. What are bias and fairness metrics?
  7. How do you measure hallucination?
  8. What is calibration in evaluation?
  9. What is truthfulness evaluation?
  10. What is diversity in generation?
  11. How do you evaluate summarization quality?
  12. What is MMLU benchmark?
  13. What is HellaSwag benchmark?
  14. How do leaderboards rank LLMs?
  15. What are limitations of benchmarks?

7. Knowledge & Memory

  1. What is a context window limit?
  2. How do LLMs handle long-context inputs?
  3. What is retrieval augmentation?
  4. What is a vector database?
  5. Why is chunking needed for documents?
  6. What are embeddings in retrieval pipelines?
  7. What is knowledge grounding?
  8. How do LLMs ?forget? knowledge?
  9. What is catastrophic forgetting in updates?
  10. What is external memory augmentation?
  11. How does RAG reduce hallucination?
  12. Explain knowledge distillation for memory.
  13. What are hybrid retrieval methods?
  14. How does FAISS work in retrieval?
  15. What is long-term memory in LLM agents?

8. Deployment & Infrastructure

  1. What is inference latency?
  2. What is batching in inference?
  3. What is model quantization?
  4. Difference between float32, float16, int8 quantization.
  5. What is distillation for deployment?
  6. How do GPUs accelerate inference?
  7. How do TPUs differ from GPUs?
  8. What is ONNX?
  9. How does TensorRT optimize models?
  10. What is model sharding?
  11. What is elastic scaling?
  12. How do you deploy LLMs in Kubernetes?
  13. How do you reduce cloud inference costs?
  14. What is serverless LLM inference?
  15. What are edge deployment challenges?

9. Safety, Ethics & Security

  1. What is bias in LLMs?
  2. How do LLMs perpetuate stereotypes?
  3. What is toxicity in outputs?
  4. How do you prevent data leakage?
  5. What is prompt injection?
  6. What are adversarial attacks?
  7. What is model stealing?
  8. What is red teaming?
  9. What is Responsible AI?
  10. What are ethical risks in healthcare use?
  11. Explain fairness in AI models.
  12. What is explainability in LLMs?
  13. How do you align models with human values?
  14. What are copyright concerns in LLM training data?
  15. What are future ethical frameworks needed?

10. Advanced & Future Directions

  1. What is Mixture of Experts (MoE)?
  2. How does sparse activation work?
  3. What is meta-learning in LLMs?
  4. What is continual lifelong learning?
  5. What is federated training for LLMs?
  6. What are neuromorphic LLMs?
  7. What is self-improving AI?
  8. What are multi-agent LLM systems?
  9. What is chain-of-thought reasoning?
  10. What is reasoning vs memorization in LLMs?
  11. How will energy efficiency shape LLM future?
  12. Open-source vs proprietary future ? which dominates?
  13. What is AGI and how close are we?
  14. How do you align LLMs with AGI goals?
  15. What is the future of alignment & safety research?

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