1200+ Gen AI For LLM's Interview Questions [2025]

The course offers over 1200 carefully curated multiple-choice questions covering all key areas of Generative AI and LLMs. Each question is accompanied by detailed explanations, ensuring learners understand not only the right answers but also the reasoning behind them.

You will explore transformer architecture, attention mechanisms, pretraining vs. fine-tuning, prompt engineering, RAG (Retrieval-Augmented Generation), zero-shot/few-shot learning, LLM evaluation metrics, deployment strategies, and more.

Topics Covered

1. Transformer Architecture

• Self-Attention Mechanism

• Scaled dot-product attention

• Multi-head attention

• Query, Key, Value operations

• Positional Encoding

• Sinusoidal vs learned

• Residual Connections and Layer Normalization

• Feedforward layers

• Encoder vs Decoder

• Causal vs Bidirectional attention

• Masked attention

2. Pretraining Objectives of LLMs

• Causal Language Modeling

• Masked Language Modeling

• Span Corruption

• Next Sentence Prediction

• Prefix Language Modeling

• Instruction-style pretraining

3. LLM Fine-Tuning Techniques

• Full Fine-tuning

• LoRA

• QLoRA

• Adapters

• Prefix Tuning

• Prompt Tuning

• PEFT

• Instruction Tuning

• FLAN, T0, Dolly, Alpaca

• SFT

4. Prompt Engineering

• Prompt Design Principles

• Clear instructions

• Context-aware phrasing

• Zero-shot, One-shot, Few-shot prompting

• Chain of Thought prompting

• Self-Consistency Decoding

• ReAct prompting

• Prompt Injection and Jailbreaks

• AutoPrompt, Soft Prompts (Prompt Tuning)

5. LLM Evaluation Metrics and Techniques

• Automatic Evaluation

• BLEU, ROUGE, METEOR, BERTScore, MoverScore

• Embedding-Based Evaluation

• Cosine similarity, dot product in embedding space

• LLM-as-a-Judge

• Human Evaluation

• Truthfulness, coherence, relevance

• Hallucination detection

• Toxicity/Bias detection

6. Decoding Strategies

• Greedy Decoding

• Beam Search

• Top-k Sampling

• Top-p (Nucleus) Sampling

• Temperature-based Sampling

• Repetition Penalty

• Contrastive Decoding

• Mixture Decoding

• Evaluation of Fluency vs Diversity

7. Embedding Models and Vector Search

• Embedding Generation Models

• Sentence-BERT

• e5, GTE, Instructor

• OpenAI text-embedding-ada

• Similarity Metrics

• Cosine similarity, dot product

• Vector Stores

• FAISS, Chroma, Weaviate, Pinecone

• Search Methods

• Dense retrieval

• Sparse retrieval (BM25)

• Hybrid search

8. Retrieval-Augmented Generation

• Chunking strategies

• Fixed-size, sliding window, recursive, semantic chunking

• Retriever architecture

• Vector-based, dense retrievers

• Prompt templates for RAG

• Fusion-in-Decoder, FiD-RAG

• Memory-efficient RAG

• Evaluation of RAG pipelines

• Latency, F1, RecallK, hallucination rate

9. LLM Agents

• Agent Frameworks

• LangChain Agents

• LangGraph (State Machine)

• ReAct (Reason + Act)

• Tool use in LLMs

• Calculator, Search, APIs

• Guardrails and Error Handling

10. Serving and Inference Optimization

• Quantization

• 8-bit, 4-bit

• GGUF format

• KV Cache

• Used for fast autoregressive decoding

• FlashAttention, xFormers

• DeepSpeed Inference, vLLM

• Serving Frameworks

• TGI, Triton, vLLM, llama.cpp, Hugging Face Inference Endpoints

11. Common LLM Failure Modes

• Hallucinations

• Token limit truncation

• Prompt injection

• Overfitting during fine-tuning

• Poor RAG retrieval

• Context window exhaustion
  
  

12. LLMOps Using AWS

And Much More!

Special emphasis is placed on interview readiness - making sure you're well-prepared for roles at top tech companies working with or on LLMs. You'll also learn about ethical concerns, AI safety, and hallucination mitigation, all of which are becoming essential in modern AI applications.

Whether you're a data science professional or a student aspiring to work in NLP or AI research, this course provides a structured, engaging, and interview-focused learning experience and ace your complex scenario-based interview.