Technical practice
LLM Architecture and Fine-tuning
What to coverExplain the architectural differences between encoder-only, decoder-only, and encoder-decoder models. Discuss when you would choose to fine-tune a pre-trained LLM versus using prompt engineering or RAG (Retrieval-Augmented Generation) for a specific business use case.
Handling LLM Hallucinations
What to coverDescribe strategies to mitigate hallucinations in LLM-based applications. Suggestion: Focus on techniques like grounding, temperature control, system prompting, and implementing verification layers in the pipeline.
RAG Pipeline Implementation
What to coverDetail the components of a robust RAG system, including document chunking strategies, vector database selection, and retrieval optimization. Explain how you evaluate the quality of the retrieved context versus the generated response.
Model Evaluation Metrics
What to coverBeyond standard accuracy, what metrics do you use to evaluate LLM performance? Discuss the trade-offs between automated metrics like BLEU/ROUGE and human-in-the-loop evaluation or model-based evaluation.
Scalability and Latency in AI Systems
What to coverHow do you optimize the inference latency of large models in a production environment? Discuss techniques such as quantization, model distillation, or caching strategies for frequent queries.
Data Privacy and Security in AI
What to coverWhat are the primary security risks when deploying LLMs in an enterprise setting? Address concerns regarding data leakage, prompt injection attacks, and ensuring PII (Personally Identifiable Information) is not included in training or context windows.
Machine Learning Lifecycle Management
What to coverDescribe your approach to MLOps for LLMs, specifically regarding version control for datasets and model weights. Suggestion: Explain how you manage the transition from experimentation in notebooks to production-grade deployment.
Tokenization and Context Windows
What to coverExplain the impact of tokenization on model performance and cost. Discuss how you handle long-context requirements and the limitations of current context window sizes in complex reasoning tasks.
Behavioural practice
Navigating Technical Ambiguity
What to coverDescribe a time when you were tasked with implementing an AI solution where the requirements were initially unclear. How did you define the scope and align stakeholders on the expected outcomes?
Managing Project Trade-offs
What to coverTell me about a situation where you had to choose between a high-performance, complex model and a simpler, more maintainable solution. How did you justify your decision to the team or client?
Cross-functional Collaboration
What to coverGive an example of how you have explained complex AI concepts to non-technical stakeholders. How did you ensure they understood the risks and limitations of the proposed technology?
Handling Technical Failure
What to coverDescribe a time when an AI model performed poorly in production or failed to meet performance benchmarks. What steps did you take to diagnose the issue and what did you learn from the experience?
Staying Current with AI Advancements
What to coverThe field of LLMs evolves rapidly. Provide an example of how you have integrated new research or tools into your workflow to improve the quality of your technical solutions.