Course ML Software Engineering: Interview Concept Review Chapter 6 Difficulty intermediate Estimated Time 900 min

Chapter 6: Generative AI & Prompting for Interviews

Generative AI & Prompting for Interviews in ML Software Engineering: Interview Concept Review.

35% complete

Learning Objectives

By the end of this chapter, you will be able to:

  • Relate Generative AI & Prompting for Interviews to common ML software engineering interview questions and trade-offs.
  • Explain when this topic deserves a deeper pass through another tutorial on this site versus staying at recap depth.
  • Surface assumptions, pitfalls, and follow-up probes an interviewer is likely to use.

← Back to course

GenAI interviewing vocabulary

An interviewer checks whether you can separate pretrained capabilities, factual grounding, hallucination mitigation, tooling cost, latency sensitivity, and evaluator design.

Prompt engineering is not meme templates—it is iterative specification with measurement against rubrics/regressions suites.

Decision lens

  • Frozen prompt: fastest if task stable + eval harness exists.
  • Retrieval: factual drift, citations, freshness—pair with grounding checks.
  • Supervised fine-tune / preference tune: repeated domain skew prompting failures.

Articulate negatives: retrieval adds infra + failure classes; fine-tuning needs data governance.

Go deeper on this site

Large Language Models (LLMs) · RAG & Retrieval Systems

1. Retrieval-first rationale?