Lilly Tech Systems
LLM & GenAI Interview Prep
The hottest interview topic in tech right now. Real questions asked at OpenAI, Anthropic, Google, Meta, and top startups — with practical answers that demonstrate the depth interviewers are looking for. From transformer internals to production LLM systems, this course covers everything you need to land an LLM/GenAI engineering role.
Your Learning Path
From understanding the GenAI interview landscape to mastering production LLM questions — each lesson covers real questions with battle-tested answers.
1. GenAI Interview Landscape
Role types (LLM engineer, GenAI engineer, AI engineer), company types hiring, what makes GenAI interviews different, and how to structure your preparation.
2. LLM Architecture Questions
15 Q&A on transformer internals, attention mechanisms, scaling laws, Mixture of Experts, positional encoding, KV cache, and architecture trade-offs.
3. Training & Alignment
12 Q&A on pre-training, SFT, RLHF, DPO, constitutional AI, reward modeling, data quality, and the full alignment pipeline.
4. Prompt Engineering Questions
12 Q&A on CoT, few-shot, system prompts, prompt injection prevention, structured output, evaluation techniques, and prompt optimization.
5. RAG & Retrieval Questions
12 Q&A on RAG architecture, chunking strategies, embedding models, vector databases, hybrid search, re-ranking, and evaluation metrics.
6. AI Agents & Tool Use
10 Q&A on agent architectures, function calling, multi-agent systems, safety guardrails, ReAct pattern, and building reliable agent systems.
7. Production LLM Questions
12 Q&A on cost optimization, latency, caching, guardrails, monitoring, fine-tuning vs RAG decisions, and scaling LLM systems.
8. Practice Questions & Tips
Rapid-fire questions, take-home project tips, FAQ accordion, and strategic advice from successful GenAI interview candidates.
What You'll Learn
By the end of this course, you will be able to:
Answer Architecture Questions
Explain transformer internals, attention mechanisms, MoE, and scaling laws with the depth that OpenAI and Anthropic interviewers expect.
Master LLM Production
Discuss cost optimization, caching, guardrails, monitoring, and the fine-tuning vs RAG trade-off like someone who has shipped LLM systems.
Design RAG & Agent Systems
Architect production-grade RAG pipelines and AI agent systems, addressing chunking, retrieval, safety, and reliability concerns.
Stand Out from Other Candidates
Use proven answer frameworks that demonstrate both practical experience and theoretical depth, with trade-off reasoning that impresses senior interviewers.