DEV Community

Cover image for 🔥 LLM Interview Series(1): What Are Large Language Models and How Do They Work
jackma
jackma

Posted on

🔥 LLM Interview Series(1): What Are Large Language Models and How Do They Work

#tutorial #ai

🔥 LLM Interview Series: What Are Large Language Models and How Do They Work

Understanding Large Language Models (LLMs) is now essential for anyone preparing for AI, ML, or data engineering interviews. These questions are designed to test both your conceptual knowledge and your ability to reason about real-world applications of LLMs.

Let’s dive into 10 expert-level interview questions—each one with detailed answers and realistic follow-ups you might face in a technical interview.

1. (Interview Question 1) What is a Large Language Model (LLM)?

Focus: Core Concept Understanding
Model Answer:
A Large Language Model (LLM) is a type of deep neural network—typically based on the Transformer architecture—that has been trained on massive text datasets to understand, generate, and manipulate human language. It uses self-supervised learning, predicting the next token in a sequence, which enables it to learn syntax, semantics, and even world knowledge from data.

Example: Models like GPT-4 or PaLM use hundreds of billions of parameters to process and generate natural language with contextual awareness.

Possible 3 Follow-ups: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)

  1. How do LLMs differ from traditional NLP models like RNNs or LSTMs?
  2. What is meant by “self-supervised” training in LLMs?
  3. Can smaller models achieve similar performance with fine-tuning?

2. (Interview Question 2) How does the Transformer architecture enable LLMs to process language effectively?

Focus: Architecture & Mechanisms
Model Answer:
Transformers rely on self-attention mechanisms that allow the model to weigh the relevance of different words in a sequence, regardless of their position. This enables parallel processing and a global understanding of context, unlike RNNs which process tokens sequentially.

Simplified Pseudocode:

Attention(Q, K, V) = softmax((Q @ K.T) / sqrt(d_k)) @ V
Enter fullscreen mode Exit fullscreen mode

Here, Q (query), K (key), and V (value) vectors represent different word embeddings.

Possible 3 Follow-ups: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)

  1. What problem does self-attention solve that RNNs struggled with?
  2. Why is positional encoding necessary in Transformers?
  3. What’s the computational cost of self-attention?

3. (Interview Question 3) What is “tokenization,” and why is it important in LLMs?

Focus: Data Preprocessing & Representation
Model Answer:
Tokenization is the process of splitting text into smaller units—tokens—that can be processed by the model. Tokens can be words, subwords, or even characters depending on the tokenizer (e.g., Byte Pair Encoding or WordPiece). It allows LLMs to represent complex language efficiently while handling unknown or rare words gracefully.

Possible 3 Follow-ups: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)

  1. How does BPE tokenization differ from WordPiece?
  2. Why do LLMs prefer subword tokenization instead of word-level?
  3. What happens if a tokenizer is poorly aligned with the training data?

4. (Interview Question 4) What is the difference between pre-training and fine-tuning in LLMs?

Focus: Model Training Stages
Model Answer:
Pre-training teaches the model general language understanding by predicting missing words or the next token in vast text corpora. Fine-tuning adapts this general model to a specific domain or task (like summarization or question answering) using smaller, labeled datasets.

Possible 3 Follow-ups: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)

  1. How is instruction tuning different from fine-tuning?
  2. Why does pre-training require unsupervised data?
  3. Can fine-tuning cause catastrophic forgetting?

5. (Interview Question 5) Explain how “attention heads” work in a Transformer model.

Focus: Multi-Head Attention Mechanism
Model Answer:
Each attention head independently focuses on different relationships or features in the input sequence—such as syntax or semantics. Multiple heads allow the model to capture diverse linguistic patterns simultaneously. Their outputs are concatenated and linearly transformed to enrich contextual understanding.

Possible 3 Follow-ups: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)

  1. What happens if you reduce the number of attention heads?
  2. How does scaling with 1/sqrt(d_k) help stabilize training?
  3. Why might certain heads be redundant?

6. (Interview Question 6) What are embeddings, and how do they help LLMs understand meaning?

Focus: Representation Learning
Model Answer:
Embeddings are dense vector representations of tokens that capture semantic similarity. Words with similar meanings have embeddings that are close in vector space. During training, embeddings are learned to optimize next-token prediction, encoding relationships like king – man + woman ≈ queen.

Possible 3 Follow-ups: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)

  1. What’s the difference between static and contextual embeddings?
  2. How can embeddings capture analogical reasoning?
  3. How are embeddings updated during fine-tuning?

7. (Interview Question 7) How do LLMs generate text during inference?

Focus: Decoding Strategies
Model Answer:
During inference, LLMs predict one token at a time using the output probabilities from a softmax layer. Techniques like greedy search, beam search, or sampling (top-k, nucleus sampling) are used to balance between determinism and creativity.

Possible 3 Follow-ups: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)

  1. What’s the trade-off between greedy search and beam search?
  2. How does temperature affect model creativity?
  3. What problem does nucleus sampling (top-p) solve?

8. (Interview Question 8) What is the role of Reinforcement Learning from Human Feedback (RLHF) in LLMs?

Focus: Human Alignment & Ethics
Model Answer:
RLHF fine-tunes an LLM using human feedback to make its responses more aligned with human values, preferences, and conversational norms. The process involves three steps: collecting human-labeled data, training a reward model, and optimizing the base model using reinforcement learning (e.g., PPO).

Possible 3 Follow-ups: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)

  1. What’s the difference between supervised fine-tuning and RLHF?
  2. How does the reward model influence the base model?
  3. What are potential biases introduced by RLHF?

9. (Interview Question 9) How do LLMs handle long context windows?

Focus: Context & Memory Efficiency
Model Answer:
LLMs use positional encodings, attention masks, and techniques like sliding windows, recurrent attention, or transformer-XL architectures to handle long sequences. More recent approaches like FlashAttention and Longformer improve efficiency by limiting attention to local or sparse regions.

Possible 3 Follow-ups: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)

  1. What is the quadratic bottleneck in attention mechanisms?
  2. How does FlashAttention improve memory usage?
  3. Why is context length critical in conversational models?

10. (Interview Question 10) What are the limitations and challenges of current LLMs?

Focus: Real-World Understanding & Critical Thinking
Model Answer:
LLMs still struggle with hallucination, reasoning consistency, bias, and interpretability. They lack true understanding—they pattern-match rather than comprehend. Moreover, their training requires massive compute resources, and their outputs can be unpredictable without careful alignment.

Possible 3 Follow-ups: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)

  1. Why do LLMs hallucinate factual information?
  2. How can we make LLMs more explainable?
  3. What are emerging research directions to reduce model bias?

Conclusion:
Mastering these 10 questions gives you a strong foundation to discuss both the theory and engineering behind large language models in interviews. If you want to go beyond static answers and experience real-time questioning, you can practice interactively with AI-driven feedback here:
👉 Try a Mock LLM Interview on OfferEasy.ai

Top comments (0)