Interview Questions

This comprehensive guide contains LLM interview questions organized by role level and technical depth. Each section progresses from foundational concepts to advanced implementation challenges, with questions sorted from simple to hard within each topic area.

Structure:

Part 1: LLM Intern - Foundation building, basic concepts, and entry-level understanding
Part 2: LLM Scientist - Research-grade development, novel architectures, and theoretical advances
Part 3: LLM Engineer - Production systems, deployment, optimization, and security

Use this guide to assess candidates at different experience levels or to prepare for LLM-focused technical interviews.

Part 1: LLM Intern 📘

Foundation building, transformer implementation, data preparation

1. Tokenization

What is a token in a language model?
What does tokenization entail, and why is it critical for LLMs?
How do LLMs manage out-of-vocabulary (OOV) words?
How do different tokenization strategies affect multilingual models?
How does the choice of tokenizer (e.g., BPE vs word-level) impact prompt design and LLM performance?

2. Embeddings

What are vector embeddings and embedding models?
What are embeddings, and how are they initialized in LLMs?
How can words be represented numerically to capture their meaning and relationships?
How are embedding models used in LLM applications?
What is the difference between embedding short and long content?
How do you benchmark embedding models on your data?
How do you improve the accuracy of embedding-based search?
How are gradients computed for embeddings in LLMs?

3. Neural Networks Fundamentals

What is a hyperparameter, and why is it important?
What is overfitting, and how can it be mitigated in LLMs?
What is the derivative of the ReLU function, and why is it significant?
What preprocessing steps are crucial when dealing with input data for LLMs?
How do hyperparameters affect the performance of LLMs?
What is the role of initialization schemes in deep learning?
How does the chain rule apply to gradient descent in LLMs?
How do transformers address the vanishing gradient problem?
How is Word2Vec trained from scratch?

4. Traditional Language Models

What is the difference between Predictive/Discriminative AI and Generative AI?
What are generative versus discriminative models in NLP?
What are n-gram models and their limitations?
How do Hidden Markov Models work in NLP?
How do LLMs differ from traditional statistical language models?

5. Transformer Architecture

What defines a Large Language Model (LLM)?
What differentiates LLMs from traditional chatbots?
What are some typical applications of LLMs?
How would you explain LLMs and their capabilities to a non-technical audience?
What is the context window in LLMs, and why does it matter?
What are positional encodings, and why are they used?
How does the attention mechanism function in transformer models?
What is multi-head attention, and how does it enhance LLMs?
How does the softmax function applied in attention mechanisms?
How does the dot product contribute to self-attention?
How are attention scores calculated in transformers?
How do encoders and decoders differ in transformers?
What are transformers and why are they important?
Describe the BERT model and its significance.
Explain the working mechanism of BERT.
LSTM vs transformers - what are the key differences?
How are LLMs trained at a high level?
Explain the self-attention mechanism and its disadvantages.
Describe the Transformer architecture in detail.
What is the role of layer normalization in transformers?
How do transformers improve on traditional Seq2Seq models?
Explain the mathematics behind positional encodings
How does dropout work mathematically and why is it effective?
How do you compute gradients through the attention mechanism?
Compare and contrast different LLM architectures, such as GPT-3 and LaMDA.

6. Data Preparation

What is chunking, and why is it important for LLM pipelines?
What factors influence chunk size?
Can you describe different approaches used for chunking text data?
How do you handle tables and lists during chunking?
How do you digitize and chunk complex documents (e.g., annual reports)?
How do you handle graphs and charts in document processing?
How do you build a production-grade document processing and indexing pipeline?

Part 2: LLM Scientist ⚙️

Research-grade model development, novel architectures, theoretical advances

7. Pre-Training

What are common pre-training objectives for LLMs and how do they work?
What is masked language modeling, and how does it aid pretraining?
What is next sentence prediction, and how does it enhance LLMs?
What are sequence-to-sequence models, and where are they applied?
How do autoregressive and masked models differ in LLM training?
What are data-parallel, model-parallel, and pipeline-parallel training schemes?
What is pipeline parallelism and how does it improve training efficiency?
How do hardware choices (TPUs vs GPUs) impact LLM training throughput and cost?
What frameworks support large-scale LLM training and how do they manage memory?
What is the Zero Redundancy Optimizer (ZeRO) and how does it enable training large LLMs?
How do scaling laws inform the design and expected performance of LLMs?
What is the mathematical relationship between model size and compute requirements?
How do scaling laws help predict the performance of larger models before training them?
Explain the mathematical foundations of optimization algorithms used in LLM training

8. Post-Training Datasets

What is fine-tuning, and why is it needed?
How do you create fine-tuning datasets for Q&A?
What are the key considerations for dataset quality and diversity?
How do you set hyperparameters for fine-tuning?
How do you estimate infrastructure requirements for fine-tuning LLMs?

9. Supervised Fine-Tuning

What is the difference between supervised fine-tuning and instruction tuning?
In what scenarios would you fine-tuning an LLM model?
When should you use fine-tuning instead of RAG?
How does transfer learning reduce training costs?
Have you fine-tuned an LLM? Describe the process.
What is catastrophic forgetting in LLMs and how can it be mitigated?
What are parameter-efficient fine-tuning (PEFT) methods?
What distinguishes LoRA from QLoRA in fine-tuning LLMs?
How can LLMs avoid catastrophic forgetting during fine-tuning?
How do parameter-efficient fine-tuning methods help prevent catastrophic forgetting?
What is the role of learning rate scheduling in LLM training?
What is the purpose of warm-up steps in training?
How do you choose the right dataset size for fine-tuning?
How do you handle data parallelism vs model parallelism?
What are the considerations for choosing sequence length during training?
Explain the difference between full fine-tuning and parameter-efficient methods
What are the trade-offs between LoRA, QLoRA, and full fine-tuning?
How would you implement gradient checkpointing for memory efficiency?

10. Preference Alignment

What is reinforcement learning and how does it relate to LLMs?
What are different preference alignment methods?
What is RLHF and what are its main challenges?
What is the difference between RLHF and RLAIF?
How does constitutional AI work for alignment?
When should you use preference alignment methods over supervised fine-tuning?
What human-centered techniques can ensure LLM alignment with user values?
What is reward hacking in RLHF and why is it problematic?
How do you implement and tune RLHF (Reinforcement Learning from Human Feedback)?
How would you approach red-teaming an LLM to identify unsafe behavior?

11. Advanced Model Architectures

What types of foundation models exist?
What are the different types of LLM architectures and their best-use scenarios?
How does PEFT mitigate catastrophic forgetting?
What are state space models and how do they compare to transformers?
How does mixture of experts (MoE) work in large language models?
How does Mixture of Experts (MoE) enhance LLM scalability?
What are the approaches for long-context modeling in LLMs?
How do you increase the context length of an LLM?
How do you optimize transformer architecture for large vocabularies?
How does Gemini optimize multimodal LLM training?

12. Reasoning & Chain-of-Thought

What is in-context learning?
What is zero-shot learning, and how do LLMs implement it?
What is few-shot learning, and what are its benefits?
What is Chain-of-Thought (CoT) prompting, and how does it aid reasoning?
How does chain-of-thought prompting improve LLM reasoning on complex tasks?
What is the Chain of Verification?
How can LLMs be adapted for logical or mathematical reasoning?
What strategies improve LLM reasoning if CoT prompting fails?
How does in-context learning work mechanistically?
How do you handle long-term dependencies in language models?

13. Evaluation & Metrics

What is perplexity and how does it relate to model quality?
What are the two primary types of evaluation metrics for LLMs?
What is the difference between intrinsic and extrinsic evaluation?
Which metrics are commonly used for summarization tasks?
What are BLEU, ROUGE, and BERT scores, and when would you use each?
How do you evaluate the best LLM model for a use case?
What metrics would you use for dialogue systems?
How do you evaluate the quality of LLM-generated text?
What are the limitations of using embedding-based metrics for evaluating LLM outputs?
What specialized metrics exist for detecting hallucinations and bias/toxicity?
How do you assess the factual accuracy of LLM outputs?
What are the challenges with automated evaluation of creative text generation?
How would you design human evaluation protocols for LLMs?

Part 3: LLM Engineer 🚀

Production systems, RAG, agents, deployment, ops & security

14. Quantization & Model Compression

What is model quantization, and how does it reduce memory usage and inference cost?
What is model pruning, and how does it improve LLM efficiency?
What is knowledge distillation, and how is it applied to compress large LLMs?
Why does quantization not always decrease LLM accuracy?
What is FP8 and what are its advantages?
What are the trade-offs of aggressive quantization on model accuracy?
How do pruning, quantization, and distillation complement each other?
How do you train LLMs with low-precision training without compromising accuracy?

15. Inference Optimization

What role does temperature play in controlling LLM output?
What are different decoding strategies for output tokens?
How do you define stopping criteria in LLMs?
How do top-k and top-p sampling differ in text generation?
How does beam search improve text generation compared to greedy decoding?
Explain the statistical properties of different sampling methods
What is speculative decoding and how does it improve inference speed?
How do you calculate the size of the key-value (KV) cache?
How do you implement and optimize KV-cache for transformer inference?
What techniques optimize LLM inference for higher throughput?
What caching strategies would you use for LLM applications?
How would you optimize LLM inference latency?
What are the key considerations for LLM model serving in production?
What are the memory and compute trade-offs in LLM deployment?
How would you implement efficient batching for LLM inference?
How would you implement constrained decoding for structured outputs?
How would you design an LLM inference system for high throughput?

16. Deployment & Infrastructure

What are the key considerations for deploying LLMs in production?
What are the trade-offs between on-premise and cloud deployment?
How can you achieve best performance while controlling costs in LLM systems?
How do you optimize the cost of an LLM system?
What are the cost optimization strategies for LLM inference?
How do you estimate the cost of running SaaS-based and open-source LLM models?
What monitoring and observability practices are important for LLM systems?
How do you handle rate limiting and resource management?
How do you handle model updates and rollbacks in production?
How would you implement auto-scaling for LLM services?
How would you handle model versioning and A/B testing for LLMs?
How do you implement failover and disaster recovery for LLM services?

17. Security & Responsible AI

What is prompt hacking, and why is it a concern?
What are different forms of hallucination in LLMs?
What are some ethical considerations surrounding LLMs?
What challenges do LLMs face in deployment?
What are the types of prompt hacking attacks?
How do you control hallucinations using prompt engineering?
What defense tactics exist against prompt hacking?
How would you fix an LLM generating biased or incorrect outputs?
What are the security considerations for LLM deployment?
How do you measure and mitigate bias in LLM outputs?
How do you control hallucinations at various levels?
Explain the concept of bias in LLM training data and its consequences
What measures can be taken to ensure data privacy and security in LLMs?
How can the explainability and interpretability of LLM decisions be improved?
In a future scenario with widespread LLM use, what ethical concerns might arise?

18. RAG (Retrieval-Augmented Generation)

What are the steps in Retrieval-Augmented Generation (RAG)?
What are the key components of a RAG system?
How does RAG improve upon standard LLM generation?
What are benefits and limitations of RAG systems?
What is a vector database, and how does it differ from traditional databases?
How is a vector store used in a RAG system, and why is ANN search important?
What is the role of chunk size and overlap in document indexing?
How do you increase accuracy and reliability in LLM outputs?
What are architecture patterns for customizing LLMs with proprietary data?
How would you choose between different embedding models for retrieval?
What design choices affect the quality of a RAG system?
How do you choose the ideal search similarity metric for a use case?
What is the difference between a vector index, vector database, and vector plugins?
How do you evaluate a RAG system’s performance?
What strategies exist for improving retrieval relevance?
What are the trade-offs between dense and sparse retrieval methods?
How would you implement re-ranking in a RAG pipeline?
What are the challenges with filtering in vector databases?
How do you determine the best vector database for your needs?
How do you handle conflicting information from multiple retrieved documents?
What are the challenges with maintaining knowledge freshness in RAG?
How do you merge and homogenize search results from multiple methods?
Explain vector search strategies such as clustering and locality-sensitive hashing
How do you handle multi-hop reasoning in RAG systems?
How do you handle multi-hop and multifaceted queries?
How do you build a production-grade RAG system?
How are contextual precision and recall defined in retrieval-augmented systems?

19. LLM Agents & Tool Use

What is in-context learning?
What is the concept of an LLM “agent”?
How can LLMs be integrated with external tools, functions, or APIs?
What are the basic concepts and strategies for implementing agents with LLMs?
How do you implement tool use and function calling in LLMs?
What is the difference between OpenAI functions and LangChain functions?
Explain ReAct prompting and its advantages
What is the Plan and Execute prompting strategy?
How do OpenAI functions compare to LangChain Agents?
How would you design a system where an LLM can retrieve information during generation?

20. Multimodal LLMs

What is a multimodal LLM and how can LLMs handle images, audio, or other modalities?
What are the benefits and challenges of multi-modal LLMs?
What architectures are used to combine vision and text in a unified model?
What challenges arise when training LLMs on multimodal data?

21. LLMOps & Production Management

What are the key metrics to monitor in production LLM systems?
What are the best practices for LLM model governance?
How do you handle model drift and performance degradation?
How do you implement automated testing for LLM applications?
How do you implement continuous integration for LLM model updates?
What are the considerations for LLM model compliance and auditing?
Design a system for fine-tuning LLMs with user data
Design a system for multi-tenant LLM serving

Advanced Topics & Specialized Areas

Prompt Engineering & In-Context Learning

What are types of prompt engineering?
Why is prompt engineering crucial for LLM performance?
What is zero-shot learning, and how do LLMs implement it?
What is few-shot learning, and what are its benefits?
What are zero-shot, few-shot, and chain-of-thought prompting techniques?
What are strategies to write good prompts?
What are some aspects to keep in mind while using few-shot prompting?
What strategies can improve prompt effectiveness and avoid common pitfalls?
What is the difference between zero-shot, few-shot learning and task-specific fine-tuning?
How can you improve the reasoning ability of your LLM through prompt engineering?
How do you iteratively refine and evaluate prompts to improve LLM output quality?
How can prompt engineering techniques mitigate hallucinations or bias in LLM outputs?

System Design Questions

ML System Design

How would you implement a chatbot system using LLMs?
How would you implement a document classification system at scale?
Design a system for automated content moderation
Design a system for automated summarization of news articles
How would you build a real-time fraud detection system?
Design a search ranking system for an e-commerce platform
Design a system for real-time language translation
How would you design a recommendation system for a streaming platform?
Design a system for personalized content recommendation at scale
How would you build a multi-modal search system (text + images)?

Specialized NLP Tasks

How does GPT-4 differ from GPT-3 in features and applications?
What specialized considerations exist when using LLMs for text summarization or Q&A?
How are evaluation metrics different for summarization vs Q&A tasks?
How does knowledge graph integration improve LLMs?
How does Adaptive Softmax optimize LLMs?

Research & Papers

What are the main contributions of GPT, BERT, and T5?
Explain the key innovations in the “Attention is All You Need” paper
What are the main findings from scaling laws papers?
What are the key insights from alignment research (InstructGPT, Constitutional AI)?
Describe the key ideas behind recent efficiency improvements (FlashAttention, etc.)
What are the key developments in efficiency and quantization research?
Describe recent advances in retrieval-augmented generation
What are the main approaches to improving reasoning capabilities?
Explain the innovations in recent multimodal models
Explain recent work on tool use and code generation
Discuss the role of LLMs in artificial general intelligence (AGI)

Coding & Implementation

Implement a basic tokenizer
Write code for computing BLEU score
Write code for top-k and top-p sampling
Implement beam search for text generation
Implement a simple RAG retrieval pipeline
Code a basic fine-tuning loop with PyTorch
Implement attention mechanism from scratch
Code gradient accumulation for large batch training
Implement KV-cache for transformer inference
Code a simple transformer decoder block
How do you mathematically analyze the stability of training?

Case Studies & Scenario-Based Questions

How can LLMs be leveraged for human-like conversations?
How do LLMs handle out-of-domain or nonsensical prompts?
Describe prompting techniques for specific enterprise use cases
What critical considerations exist when a client wants to use an LLM for customer service?
In a virtual assistant application, how do you handle user requests you cannot understand?
In a customer service chatbot application, how do you stay up-to-date with new product information?
How would you approach fine-tuning an LLM for creative content generation?
If an LLM is generating offensive or inaccurate outputs, how would you diagnose and address the issue?
Design an LLM chat assistant with dynamic context
If tasked with training an LLM to predict stock market trends, how would you address the challenge of differentiating between causal factors and correlations?
Explore future applications of LLMs in various industries

Behavioral Questions (Use STAR Framework)

Tell me about a time when you had to debug a complex ML model performance issue
Describe a situation where you had to work with incomplete or noisy data
Give an example of when you had to explain a complex technical concept to non-technical stakeholders
Tell me about a time when you disagreed with a technical decision on your team
Describe a challenging project where you had to learn new technologies quickly
Give an example of when you had to optimize a model for production constraints
Tell me about a time when you had to handle conflicting priorities in a project
Describe a situation where you had to collaborate with cross-functional teams
Give an example of when you had to make a trade-off between model accuracy and latency
Tell me about a time when you had to recover from a failed experiment or approach

Super Hard Challenges

Convert nf4 / BnB 4bit to Triton
Make FSDP2 work with QLoRA
Remove graph breaks in torch.compile
Help solve Unsloth issues!
Memory Efficient Backprop