Machine Learning System Design: Post-GenAI (Part 1)

Since the advent of generative AI and large language models (LLMs), ML system design interviews have evolved. Previously, interviews focused on traditional ML model design, feature engineering, and evaluation metrics. Now, there’s an emphasis on:

• Prompt Engineering: Candidates need to showcase their ability to craft effective prompts to elicit desired outputs from LLMs.
• System Design with LLMs: Interviews delve into how to integrate LLMs into broader systems, addressing scalability, reliability, and security.
• Hallucination and Bias: Candidates are assessed on their understanding of LLM limitations, such as hallucinations and biases, and strategies to mitigate them.
• Ethical Considerations: The ethical implications of using generative AI and ensuring fairness and responsible usage are now key discussion points.

Overall, the focus has shifted towards evaluating a candidate’s ability to leverage the unique capabilities and challenges of LLMs in real-world ML systems.

Before LLMs

Traditional ML Focus: Interviews centered on building models for classification, regression, or clustering tasks.

• Example question: “Design a recommendation system for a streaming service.”
• Feature Engineering: Candidates needed expertise in data preprocessing, feature selection, and extraction techniques.
• Evaluation: Emphasis was placed on model performance metrics like accuracy, precision, recall, and F1-score.

After LLMs:

1. Prompt Engineering as a Core Skill:

• What it is: The art and science of designing prompts that elicit specific, accurate, and relevant responses from LLMs.
• Interview Focus: Candidates may be asked to write prompts for various tasks, such as summarization, translation, or creative text generation. They may also be evaluated on their ability to iterate on prompts and optimize their performance.
• Example Question: “Write a prompt for an LLM to generate a concise summary of a long news article, while maintaining key information and avoiding factual errors.”

2. LLM Integration into Larger Systems:

• What it is: Building applications that seamlessly incorporate LLMs for tasks like chatbots, content generation, and code completion.
• Interview Focus: Candidates are asked to design systems that handle large-scale user requests, manage LLM limitations (like context windows), and ensure the security and privacy of user data.
• Example Question: “Design a customer support chatbot using an LLM. How would you handle user input, generate responses, and measure the chatbot’s effectiveness?”

3. Addressing LLM Limitations and Biases

• What it is: LLMs can hallucinate (generate incorrect information), exhibit biases present in training data, and struggle with complex reasoning.
• Interview Focus: Candidates need to demonstrate knowledge of these limitations and propose strategies to mitigate them. This might involve using external knowledge sources, filtering outputs, or training LLMs on more balanced datasets.
• Example Question: “Discuss potential biases in an LLM trained on social media data. How would you design a system to minimize these biases?”

4. Ethical and Responsible AI Design:

• What it is: Ensuring that LLM-powered systems are fair, transparent, and accountable.
• Interview Focus: Candidates may be asked about ethical considerations in LLM development, such as potential for misinformation, discrimination, or job displacement. They may also be asked to discuss how they would build safeguards into their systems to prevent misuse.
• Example Question: “What are the ethical implications of using an LLM to generate news articles? How would you design a system to ensure responsible use of this technology?”

Final Thoughts

ML system design interviews now encompass a broader range of topics beyond traditional ML models. Prompt engineering, LLM integration, bias mitigation, and ethical considerations are crucial areas that candidates need to master to succeed in this evolving field.