ML Interview Questions

Explain how you would optimize prompts for different Large Language Model (LLM) architectures, such as GPT, Claude, and Llama. Discuss the differences in approach and why certain strategies might be more effective for one model over another.

Explain how you would design a system to prevent prompt injection attacks and jailbreaking attempts in large language model (LLM) applications. Discuss both theoretical approaches and practical techniques.

What are some effective techniques for designing prompts that maintain context and coherence in multi-turn conversations? Discuss how these techniques can be applied in practical scenarios.

How do you evaluate the effectiveness of prompts in machine learning models, specifically in the context of prompt engineering? Describe the methodologies and metrics you would use to determine whether a prompt is performing optimally, and explain how you would test and iterate on prompts to improve their effectiveness.

Describe how Retrieval-Augmented Generation (RAG) uses prompt templates to enhance language model performance. What are the implementation challenges associated with RAG, and how can it be effectively integrated with large language models?

Describe chain-of-thought prompting in the context of improving language model reasoning abilities. How does it relate to few-shot prompting, and when is it particularly useful?

In the context of Natural Language Processing (NLP), how is transfer learning applied? Discuss its benefits and provide examples of models or techniques that utilize transfer learning effectively.

Explain the difference between stemming and lemmatization in Natural Language Processing (NLP). Provide examples of how each is used in practice and discuss any advantages or disadvantages they may have.

What is Named Entity Recognition (NER), and what are some of the common approaches used to tackle this task in Natural Language Processing? Discuss the role of NER in Information Extraction and how it relates to sequence labeling.

Describe the attention mechanism and discuss its significance in the architecture of Transformer models for NLP tasks.