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34 Essential LLM Interview Questions Across Architecture, Training, and Optimization
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Inflated / Uruttu
Original Content
Master these LLM questions before your next interview 👇
40 questions, grouped the way a real LLM interview actually moves — definitions first, production last. Save this and work through one topic a day.
1) LLMs: Fundamentals & Comparison
• What are Large Language Models (LLMs)?
• How are LLMs different from traditional language models?
• What are Foundation Models?
2) Tokenization & Embeddings
• What is tokenization, and why is it important in LLMs?
• What role do embeddings play in LLMs?
• How do LLMs handle out-of-vocabulary (OOV) words?
3) Encoder–Decoder & Pretraining Objectives
• What are Sequence-to-Sequence (Seq2Seq) models?
• What is the difference between an encoder and a decoder?
• How do autoregressive models differ from masked models?
• What is Next Sentence Prediction (NSP)?
4) Transformer Architecture & Attention
• How does the Transformer overcome the challenges of traditional Seq2Seq models?
• What are positional encodings in LLMs?
• What is attention in Transformer models?
• What is Multi-Head Attention?
• How are attention scores computed in Transformers?
• What is the softmax function, and why is it used in attention?
• How is the dot product used in self-attention?
5) Decoding & Sampling Strategies
• What is beam search, and how does it differ from greedy decoding?
• Explain the concept of temperature in LLM text generation.
• What is the difference between top-k sampling and top-p sampling?
• How does Adaptive Softmax speed up LLMs?
6) Loss Functions, Gradients & Backpropagation
• What is cross-entropy loss, and why is it used in language models?
• How are gradients computed with respect to embeddings?
• What is the role of the Jacobian matrix in backpropagation?
• What is the vanishing gradient problem, and how do Transformers solve it?
• What are eigenvalues and eigenvectors, and how are they used in dimensionality reduction?
• How is KL divergence used in evaluating LLMs?
7) Fine-Tuning & Parameter-Efficient Training
• What are LoRA and QLoRA?
• How can catastrophic forgetting be mitigated in LLMs?
• How does Parameter-Efficient Fine-Tuning (PEFT) prevent catastrophic forgetting?
• What is model distillation, and how is it applied to LLMs?
8) Overfitting & AI Model Types
• What is overfitting, and how can it be prevented?
• What are Generative and Discriminative models?
• What is the difference between Discriminative AI and Generative AI?
Want detailed answers to all 40?
Every question is fully solved inside the 22 course AI Interview Master Bundle — 1200+ interview questions with answers, company-wise question sets, and beginner-friendly, scenario-first explanations - https://lnkd.in/g9GVkihi
Validated Content
Confirmed Accurate
The 34 listed questions cover genuinely relevant LLM/Transformer interview topics. Tokenization, embeddings, encoder-decoder architecture, self-attention, beam search, temperature, top-k/top-p sampling, cross-entropy loss, LoRA/QLoRA, catastrophic forgetting, model distillation, overfitting, and generative vs. discriminative models are all standard, well-established concepts in modern AI engineering interviews.
The distinction between autoregressive (e.g., GPT) and masked (e.g., BERT) models is correctly framed.
Beam search, greedy decoding, temperature, and sampling strategies are accurately described as core text generation concepts.
LoRA and QLoRA are correctly identified as parameter-efficient fine-tuning techniques.
Mostly Accurate
"grouped the way a real LLM interview actually moves — definitions first, production last." Real interviews at companies like OpenAI, Anthropic, Google DeepMind, and Meta do cover these topics, but they rarely follow this rigid linear structure. Interviewers typically jump between theory, implementation, and production trade-offs unpredictably.
"40 questions, grouped..." The post claims 40 questions, but only 34 are explicitly listed in the text. The remaining 6 may have been in the images (which lacked alt text), making the count misleading for text-only readers.
Partially Accurate
"How does Parameter-Efficient Fine-Tuning (PEFT) prevent catastrophic forgetting?" This framing is misleading. PEFT methods (like LoRA) can mitigate catastrophic forgetting by freezing most base weights, but PEFT is not primarily designed as a catastrophic forgetting solution. Rehearsal, EWC, and modular approaches are the standard mitigation techniques.
"How is KL divergence used in evaluating LLMs?" KL divergence is primarily used during training (e.g., in RLHF/PPO as a constraint, or in distillation), not as a standard evaluation metric. Perplexity, BLEU, ROUGE, and human preference frameworks are more typical for evaluation.
"How does Adaptive Softmax speed up LLMs?" Adaptive Softmax is a training optimization for large vocabulary classification, but it is a niche technique and not a mainstream topic in modern LLM engineering interviews.
"What are eigenvalues and eigenvectors, and how are they used in dimensionality reduction?" This is a general machine learning/math question, not specifically LLM-relevant. Dimensionality reduction is rarely a focus in LLM interviews.
The post claims to cover "definitions first, production last," but omits critical production-level topics that dominate real interviews: inference optimization (KV cache, speculative decoding, quantization), RAG, agents, RLHF, scaling laws, system design, and safety/alignment.
Not Fully Verified
"22 course AI Interview Master Bundle — 1200+ interview questions with answers, company-wise question sets, and beginner-friendly, scenario-first explanations." The linked bundle (lnkd.in/g9GVkihi) cannot be independently verified for content quality, accuracy, or whether it actually contains 1200+ questions. The claim is unverifiable without purchase.
"Every question is fully solved inside..." Cannot be confirmed.
Opinion / Promotional Language
"Master these LLM questions before your next interview" — Engagement hook with no objective basis.
"grouped the way a real LLM interview actually moves" — Unsubstantiated claim about interview structure.
"Save this and work through one topic a day" — Classic LinkedIn engagement bait.
"Want detailed answers to all 40?" — Lead-in to paid product promotion.
"beginner-friendly, scenario-first explanations" — Marketing descriptor for the bundle.
The entire post functions as a lead magnet for a paid course, which is not disclosed upfront.
Missing Context
No disclosure that this is a promotional post for a paid product.
No mention that equivalent question sets are freely available in numerous GitHub repositories, blogs, and academic resources.
No caveats that interview formats vary dramatically by company (research labs vs. startups vs. big tech).
Missing modern high-priority topics: RAG, agentic systems, RLHF, inference serving, scaling laws (Chinchilla), MoE architectures, and evaluation frameworks.
No acknowledgment that hands-on implementation (building transformers from scratch, profiling inference) is often more valuable than memorizing definitions
The 34 listed questions cover genuinely relevant LLM/Transformer interview topics. Tokenization, embeddings, encoder-decoder architecture, self-attention, beam search, temperature, top-k/top-p sampling, cross-entropy loss, LoRA/QLoRA, catastrophic forgetting, model distillation, overfitting, and generative vs. discriminative models are all standard, well-established concepts in modern AI engineering interviews.
The distinction between autoregressive (e.g., GPT) and masked (e.g., BERT) models is correctly framed.
Beam search, greedy decoding, temperature, and sampling strategies are accurately described as core text generation concepts.
LoRA and QLoRA are correctly identified as parameter-efficient fine-tuning techniques.
Mostly Accurate
"grouped the way a real LLM interview actually moves — definitions first, production last." Real interviews at companies like OpenAI, Anthropic, Google DeepMind, and Meta do cover these topics, but they rarely follow this rigid linear structure. Interviewers typically jump between theory, implementation, and production trade-offs unpredictably.
"40 questions, grouped..." The post claims 40 questions, but only 34 are explicitly listed in the text. The remaining 6 may have been in the images (which lacked alt text), making the count misleading for text-only readers.
Partially Accurate
"How does Parameter-Efficient Fine-Tuning (PEFT) prevent catastrophic forgetting?" This framing is misleading. PEFT methods (like LoRA) can mitigate catastrophic forgetting by freezing most base weights, but PEFT is not primarily designed as a catastrophic forgetting solution. Rehearsal, EWC, and modular approaches are the standard mitigation techniques.
"How is KL divergence used in evaluating LLMs?" KL divergence is primarily used during training (e.g., in RLHF/PPO as a constraint, or in distillation), not as a standard evaluation metric. Perplexity, BLEU, ROUGE, and human preference frameworks are more typical for evaluation.
"How does Adaptive Softmax speed up LLMs?" Adaptive Softmax is a training optimization for large vocabulary classification, but it is a niche technique and not a mainstream topic in modern LLM engineering interviews.
"What are eigenvalues and eigenvectors, and how are they used in dimensionality reduction?" This is a general machine learning/math question, not specifically LLM-relevant. Dimensionality reduction is rarely a focus in LLM interviews.
The post claims to cover "definitions first, production last," but omits critical production-level topics that dominate real interviews: inference optimization (KV cache, speculative decoding, quantization), RAG, agents, RLHF, scaling laws, system design, and safety/alignment.
Not Fully Verified
"22 course AI Interview Master Bundle — 1200+ interview questions with answers, company-wise question sets, and beginner-friendly, scenario-first explanations." The linked bundle (lnkd.in/g9GVkihi) cannot be independently verified for content quality, accuracy, or whether it actually contains 1200+ questions. The claim is unverifiable without purchase.
"Every question is fully solved inside..." Cannot be confirmed.
Opinion / Promotional Language
"Master these LLM questions before your next interview" — Engagement hook with no objective basis.
"grouped the way a real LLM interview actually moves" — Unsubstantiated claim about interview structure.
"Save this and work through one topic a day" — Classic LinkedIn engagement bait.
"Want detailed answers to all 40?" — Lead-in to paid product promotion.
"beginner-friendly, scenario-first explanations" — Marketing descriptor for the bundle.
The entire post functions as a lead magnet for a paid course, which is not disclosed upfront.
Missing Context
No disclosure that this is a promotional post for a paid product.
No mention that equivalent question sets are freely available in numerous GitHub repositories, blogs, and academic resources.
No caveats that interview formats vary dramatically by company (research labs vs. startups vs. big tech).
Missing modern high-priority topics: RAG, agentic systems, RLHF, inference serving, scaling laws (Chinchilla), MoE architectures, and evaluation frameworks.
No acknowledgment that hands-on implementation (building transformers from scratch, profiling inference) is often more valuable than memorizing definitions