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

Explore the world of Generative AI and Large Language Models (LLMs) with comprehensive training covering over 1200 multiple-choice questions. Each question comes with detailed explanations to solidify your understanding of key concepts and reasoning. This course delves into the core components of LLMs, including transformer architecture, attention mechanisms, and the crucial distinctions between pretraining and fine-tuning.

Key Topics Covered:

• Transformer Architecture: Master the fundamentals of the self-attention mechanism, including scaled dot-product attention and multi-head attention. Understand query, key, value operations, positional encoding, and the roles of residual connections and layer normalization. Explore encoder vs decoder structures, causal vs bidirectional attention, and masked attention.
• LLM Pretraining Objectives: Learn about various pretraining objectives used for LLMs, such as causal language modeling, masked language modeling, span corruption, next sentence prediction, and prefix language modeling. Understand instruction-style pretraining techniques.
• LLM Fine-Tuning Techniques: Dive into effective LLM fine-tuning techniques, including full fine-tuning, LoRA, QLoRA, Adapters, Prefix Tuning, Prompt Tuning (PEFT), and instruction tuning using methods like FLAN, T0, Dolly, and Alpaca and SFT.
• Prompt Engineering: Develop expert prompt engineering skills. Learn prompt design principles for creating clear instructions and context-aware phrasing. Explore zero-shot, one-shot, and few-shot prompting, chain of thought prompting, self-consistency decoding, ReAct prompting, and how to avoid prompt injection and jailbreaks. Discover AutoPrompt and Soft Prompts (Prompt Tuning).
• LLM Evaluation: Understand LLM evaluation metrics and techniques, including automatic evaluation using metrics like BLEU, ROUGE, METEOR, and BERTScore, and embedding-based evaluation using cosine similarity. Learn about human evaluation focusing on truthfulness, coherence, relevance, and detecting hallucinations and toxicity/bias.
• Decoding Strategies: Explore various decoding strategies such as greedy decoding, beam search, top-k sampling, top-p (nucleus) sampling, temperature-based sampling, repetition penalty, and contrastive decoding. Understand mixture decoding and how to evaluate fluency vs diversity.
• Embedding Models & Vector Search: Learn about embedding generation models like Sentence-BERTe5, GTE, and Instructor. Understand similarity metrics like cosine similarity and dot product. Explore vector stores like FAISS, Chroma, Weaviate, and Pinecone and different search methods including dense retrieval, sparse retrieval (BM25), and hybrid search.
• Retrieval-Augmented Generation (RAG): Master Retrieval-Augmented Generation (RAG). Learn about chunking strategies (fixed-size, sliding window, recursive, semantic) and retriever architectures (vector-based, dense retrievers). Understand prompt templates for RAG and fusion-in-decoder (FiD-RAG). Learn to evaluate RAG pipelines based on latency, F1, Recall, K, and hallucination rate.
• LLM Agents: Build intelligent LLM agents using frameworks like LangChain Agents, LangGraph (State Machine), and ReAct (Reason + Act). Learn about tool use in LLMs (calculators, search, APIs) and implement guardrails and error handling.
• Serving & Inference Optimization: Optimize LLM performance for deployment. Learn about quantization (8-bit, 4-bit), GGUF format, and the use of KV Cache for fast autoregressive decoding. Explore FlashAttention, xFormers, and DeepSpeed Inference, vLLMS. Understand various serving frameworks including TGI, Triton, vLLM, llama.cpp, and Hugging Face Inference Endpoints.
• Common LLM Failure Modes: Identify and mitigate common LLM failure modes such as hallucinations, token limit truncation, prompt injection, overfitting during fine-tuning, poor RAG retrieval, and context window exhaustion.
• LLMOps: Gain insights into LLMOps practices,