Modern LLM Engineering Interview Concepts to Master

 

 

Modern LLM Engineering Interview Concepts to Master

1. How Large Language Models Work (Transformers, Attention, Tokens)
2. Prompt Engineering vs Fine-Tuning (When to Use Each)
3. Few-Shot, Zero-Shot & In-Context Learning
4. Embeddings: What They Are & How They’re Used
5. Vector Databases & Similarity Search
6. Retrieval-Augmented Generation (RAG) Architecture
7. Chunking Strategies & Context Window Tradeoffs
8. Hallucinations: Causes, Detection & Mitigation
9. LLM Evaluation Metrics (Accuracy, Relevance, Faithfulness)
10. Tool Calling & Function Calling Use Cases
11. Agents vs Simple LLM Pipelines
12. Memory in LLM Systems (Short-Term vs Long-Term)
13. Latency vs Cost Tradeoffs in LLM Apps
14. Scaling LLM Applications for Production
15. Model Selection (Closed vs Open-Source Models)
16. Security Risks (Prompt Injection, Data Leakage)
17. Guardrails & Safety Mechanisms
18. Observability & Monitoring for LLMs
19. Multi-Modal LLM Systems (Text, Vision, Audio)
20. Real-World LLM System Design Questions