LLM Interview Questions: Complete Guide for AI Engineers
Comprehensive guide to LLM interview questions covering architecture, fine-tuning, RAG systems, and production deployment.
LLM and AI engineering roles are among the most sought-after positions in 2025. This guide covers the essential questions you need to master.
Transformer Architecture
1. Explain the Transformer architecture
Transformers use self-attention mechanisms instead of recurrence. Key components: multi-head attention, positional encoding, feed-forward networks, and layer normalization. The architecture enables parallelization and captures long-range dependencies.
2. What is self-attention and how does it work?
Self-attention computes attention scores between all positions in a sequence. For each position, it creates Query, Key, and Value vectors, computes attention weights via scaled dot-product, and produces weighted sum of values. Complexity is O(n^2) for sequence length n.
3. Explain the difference between encoder-only, decoder-only, and encoder-decoder models
Model architectures:
- Encoder-only (BERT): Bidirectional, good for classification, NER
- Decoder-only (GPT): Autoregressive, good for generation
- Encoder-decoder (T5): Good for seq2seq tasks like translation
Fine-tuning and Training
4. What is the difference between fine-tuning, LoRA, and prompt tuning?
Training approaches:
- Full fine-tuning: Update all parameters, expensive but effective
- LoRA: Low-rank adaptation, trains small matrices, memory efficient
- Prompt tuning: Learn soft prompts, freeze model weights
- QLoRA: Quantized LoRA, even more memory efficient
5. What is RLHF and why is it important?
Reinforcement Learning from Human Feedback trains a reward model from human preferences, then uses PPO to fine-tune the LLM. It aligns model outputs with human values and preferences. Alternatives include DPO (Direct Preference Optimization).
RAG and Production
6. Explain RAG (Retrieval-Augmented Generation)
RAG combines retrieval and generation: embed documents into vector store, retrieve relevant chunks for query, inject context into LLM prompt. Benefits: reduced hallucination, up-to-date information, traceable sources.
7. How do you evaluate LLM outputs?
Evaluation methods:
- Automated metrics: BLEU, ROUGE, BERTScore, perplexity
- LLM-as-judge: Use stronger LLM to evaluate outputs
- Human evaluation: Quality, helpfulness, safety ratings
- Task-specific: Accuracy, F1, exact match for structured tasks
Practice LLM interview questions with our AI Mock Interview. Test your knowledge of transformers, fine-tuning, and RAG systems.
Practice with AI Mock Interviews
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