ML Interview Questions

Explain the role of system prompts in machine learning models and discuss best practices for designing effective system prompts. Consider both theoretical and practical aspects in your explanation.

What is retrieval-augmented prompting and how can it be effectively implemented in natural language processing tasks?

Explain few-shot prompting with examples and its effectiveness compared to zero-shot.

**What are the key elements and strategies that contribute to crafting an effective prompt for Large Language Models (LLMs)?**

What are prompt templates, and how can they be effectively designed to be reusable in natural language processing tasks? Discuss their importance and provide examples of how they can be implemented in practice.

Explain how chain-of-thought prompting works in the context of prompt engineering for language models. Discuss its advantages, potential use cases, and any limitations it may have.

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.