Вопрос про production ML
What is your view on using modern GenAI or vibe-coding tools for software and ML work, and where do they fail today?
Ответить самому
Сначала сформулируйте ответ как на собеседовании, затем откройте разбор и оцените себя.
Короткий ответ
Use GenAI as a productivity tool for autocomplete, boilerplate, refactoring and drafts, but keep engineering review because hallucinated APIs, weak specs and untested assumptions still break production.
Полный разбор
A balanced answer is better than hype or dismissal. GenAI coding tools are useful for autocomplete, boilerplate, tests, refactors, documentation, small scripts and exploring unfamiliar APIs. They can turn a precise plan into code faster.
They fail when the task is underspecified, context is missing, APIs are hallucinated, system constraints are implicit, or correctness needs domain judgment. In ML work, a generated service can look plausible while mishandling monitoring, data contracts, privacy or edge cases.
The practical workflow is to treat LLMs as programming tools, not owners. Provide precise context, decompose the task, ask for tests, review the diff, run checks and keep humans accountable for product and production correctness. Better prompts help, but writing precise instructions is still engineering.
Теория
LLM coding shifts some effort from typing code to specifying, reviewing and verifying code.
Типичные ошибки
- Say LLMs can already build production systems without review.
- Dismiss them entirely because they hallucinate.
- Forget tests and runtime verification.
- Use vague prompts and blame only the model for bad output.
Как отвечать на собеседовании
- Give one useful use case and one concrete failure mode.
- End with verification as the non-negotiable step.