GenAI Engineer Interview Prep: RAG, Embeddings, LLM Agents

About this course

Everyone can define RAG, embeddings, agents, fine-tuning, and "LLM eval.

" Far fewer can survive the second question, the "why does this happen, and what would you do about it" kind that quietly decides interviews.

This course is built around that gap. It collects the toughest, most commonly asked interview questions across the five pillars of modern GenAI engineering and answers each one the way a strong candidate should: derive the mechanism instead of naming it, put a number on it (bytes, recall, latency, GPU memory), and state the tradeoff you're accepting.

Everything is organized into three tiers that build on each other, Beginner foundations, Intermediate mechanisms, and Advanced/Architect-level system design, across five modules:

Retrieval-Augmented Generation (RAG): chunking, hybrid search and Reciprocal Rank Fusion, re-rankers, "lost in the middle," knowledge conflict, and designing RAG for 500M+ documents under 200ms.

Transformers : Tokens and embeddings, Attention, Multi-head attention, KV cache, GQA, RoPE, FlashAttention & MoE ,PagedAttentionEmbeddings & Vector Databases: the distributional hypothesis, anisotropy, HNSW, product quantization, Matryoshka embeddings, sharding to a billion vectors, drift, and zero-downtime re-indexing. LLM Agents & Agentic Systems: the agent loop, tools, memory design, MCP, orchestration, multi-agent systems, long-horizon autonomy, security, and observability.

LLM Fine-Tuning: pretraining vs SFT vs alignment, loss masking, chat templates, LoRA/QLoRA/DPO, the GPU memory math, distributed training with ZeRO and FSDP, and serving many fine-tunes at once.

LLM Evaluation: offline vs online eval, LLM-as-a-judge and its failure modes, RAG-specific metrics (faithfulness, answer relevance, context precision/recall), benchmark contamination, regressi

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