01LLM InterviewsComplete roadmap for LLM interview preparation - 11 topics from Transformer internals to safety guardrails, with dependency maps, study paths, and self-assessment.02Transformer Internals for LLMsMaster decoder-only architecture, causal masking, RoPE, GQA, SwiGLU, RMSNorm, KV cache mechanics, parameter counting, and FLOP estimation for LLM interviews.03LLM PretrainingMaster data pipelines, tokenization, training objectives, scaling laws, compute budgets, and distributed training infrastructure for LLM interviews.04Fine-Tuning StrategiesFull fine-tuning, LoRA, QLoRA, prefix tuning, adapters - the complete guide to adapting LLMs for production, with interview-ready depth.05RLHF and AlignmentReward modeling, PPO, DPO, constitutional AI - alignment techniques explained for ML interviews.06RAG SystemsRetrieval-augmented generation - chunking, embedding, vector databases, retrieval, reranking, and evaluation.07Prompt EngineeringChain-of-thought, few-shot, structured prompting, prompt injection - systematic prompt design for LLM interviews.08LLM EvaluationBenchmarks, human evaluation, LLM-as-judge, custom evaluation frameworks, red teaming, and production metrics for large language models.09LLM Inference OptimizationKV cache, continuous batching, speculative decoding, quantization, and model serving - everything you need for the inference optimization interview.10Agent ArchitecturesReAct, function calling, tool use, MCP, multi-agent systems, planning, memory - the complete agent interview guide.11Safety and GuardrailsPrompt injection, jailbreaking, content filtering, red teaming, PII handling, and responsible AI governance - the safety interview guide.12LLM Interview Questions50+ LLM-specific interview questions with detailed answers.
