Every topic from your LLM ENGINEER.md (brother's real interview) is covered here:

Your source topic	Where answered
vLLM architecture / PagedAttention	Q72
KV caching	Q70, code Q107
Continuous batching	Q73
End-to-end fine-tuning pipeline	Q50c
LoRA vs QLoRA key differences	Q50b
QLoRA quantization mechanics	Q49, Q50
Rank r impact on Wq/Wk/Wv & param count	Q48
Catastrophic forgetting	Q37
RLHF dataset prep	Q56
PPO vs DPO	Q59
KL divergence role / 0 vs high / token-level	Q57, Q58
DPO near-zero-probability edge case	Q60
Transformer architecture flow	Q1
Mistral vs GPT (GQA, sliding window)	Q16b
Multi-Head Attention	Q11
Attention vs FlashAttention	Q14
VLM: projecting image features (ViT/CLIP)	Q93, Q94
Audio/visual LLMs	Q95
DeepSpeed vs TGI	Q98
Docker Swarm	Q100
Gradient descent / loss functions	`02_MACHINE_LEARNING_HANDBOOK.md` §7–8
TTS/STT loss functions	Q97
GIL / asyncio / concurrency vs parallelism	`01_PYTHON_HANDBOOK.md` §7–8

Coverage of the LLM-Interview-Questions-and-Answers-Hub (Kalyan KS, 115 Q) — mapped in full:

Hub topic (Q#)	Where
Positional embeddings why / inference steps / KV cache (1–3)	Q25, Q1+Q7d, Q70
Quantization / KV-cache memory / tokens→embeddings (4–6)	Q64, Q69+Q78d, Q21
Subword tokenization / large-vocab tradeoffs / self-attention steps (7–9)	Q18, Q22, Q8
Attention complexity / vanishing gradient / tokenization (10–12)	Q6, Q7b, Q18
Token & embedding layer / self-attention role (13–15)	Q21, Q8
Encoder / decoder / encoder-decoder (16–18)	Q2
Scaling √dₖ / why multi-head / combining heads (19–21)	Q10, Q11, Q17c
Masked self-attention / pros-cons / decoder masking (22–24)	Q12, Q17d
How masking works / cross-attention / softmax (25–27)	Q12, Q13, Q17b
Residual / layer norm / cross-entropy (28–30)	Q5, Q30
Transformers vs RNN / limitations / vs CNN-RNN (31–33)	Q3, Q17d, Q7c
Vanishing gradient / FFN / training vs inference (34–36)	Q7b, Q4, Q7d
Latency / batch inference / batching / MoE / decoding (37–42)	Q74, Q73, Q80, Q39
Decoding strategies: greedy/beam/deterministic-stochastic/temp/context (43–54)	Q39–Q44e
Temperature / autoregressive / DLM / streaming (55–60)	Q40, Q30, Q101, Q45
Speculative decoding / distributed / Flash Attn / continuous batching / mixed precision (61–66)	Q43, Q78c, Q14, Q73, Q78d
Online/offline / throughput-latency / bottlenecks / measure / engines (67–72)	Q78b, Q75, Q76, Q74, Q77
Accelerate inference / CoT / prompt eng / zero-few-shot (73–78)	Q79, Q85e, Q85b, Q85c
Few-shot / context / system prompt / ICL / self-consistency / hallucination / JSON / ReAct (79–87)	Q85c–Q85o
LLM phases / FT types / instruction & alignment tuning / overfitting / forgetting / full-FT / PEFT (88–96)	Q32, Q33, Q54b, Q37, Q46
FT vs RAG / LoRA / QLoRA / preference alignment / grad-accum / speed-up FT (97–108)	Q52, Q47, Q49, Q59, Q54c
Pretraining objective / causal vs masked / OOV / scaling law / MoE / model parallelism / self-supervised (109–117)	Q30, Q31, Q23, Q34, Q80, Q38

All 115 hub questions are answered above with added system-level and mathematical depth plus code. Use both: read this for understanding, drill the hub's exact phrasings.

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