In traditional software hiring, interviews typically centred on things like languages mastered, frameworks used and algorithms memorised, but things have changed now. As the rise of AI is changing coding, it is also changing what makes an engineering candidate stand out. Today, as leaders in professional vibe coding, businesses find themselves asking: What should we be interviewing for?
In almost all cases, companies should first look for candidates who have already jumped into the AI pool. If someone has not at least experimented with vibe coding yet, that’s a potential red flag. Understand vibe coding and how you can hire in the age of AI.
What is Vibe Coding?
It is a software development method popularised in early 2025, where users create applications by instructing AI assistants using natural language, rather than writing code manually. It focuses on the “vibe” or high-level goals, relying on AI to generate, test, and debug code while the user oversees the results.
- Code creation in Vibe coding is AI-generated from natural language prompts.
- In this, the coding expertise required is lower than in traditional programming.
- Development speed is potentially faster, particularly for prototyping simpler tasks.
- Code maintainability heavily depends on AI output quality and user review.
Your interview must be focused on vibe coding.
Key Skills to Interview For
First and foremost, the step is to look after the skills. The skills of a candidate can directly reflect the company’s success and the ability to fight future challenges. Here are the skills you need to look for in future hiring:
- AI Literacy and Prompt Engineering: The ability to use AI tools (ChatGPT, GitHub Copilot) to improve productivity and quality, including crafting effective prompts to get accurate results.
- Critical Thinking: Candidates must be able to evaluate AI-generated outputs for accuracy, bias, and context, rather than blindly trusting them.
- Adaptability and Lifelong Learning: The capacity to learn new tools and adapt to changing workflows as AI technologies evolve rapidly.
- Emotional Intelligence and Empathy: Skills that AI cannot replicate, such as understanding nuance, stakeholder management, and compassionate decision-making.
- AI Ethics and Responsibility: Awareness of AI limitations, data privacy, and potential biases in automated systems.
These skills are vital to look for when hiring in the era of AI.
Ask Surface Lived Experience, Not Memorised Answers
Relying on lived experience rather than memorised answers is a superior strategy for interviews because it demonstrates authenticity, judgment, and the ability to operate in real-world scenarios.
Memorised scripts often fall apart under follow-up questions, whereas authentic stories about personal, professional, or academic experiences show true understanding and resilience.
To uncover true lived experience rather than rehearsed, “canned” answers, an interviewer must move beyond standard questions and adopt a conversational approach that focuses on the processes, emotions, and specific decision-making moments of a candidate’s past.
Here are some examples:
- “Tell me about a time you made a mistake…”, Instead, you can ask: “Describe a mistake you made, but specifically, at what point did you realise it was a mistake, and what was the immediate thought process to correct it?”.
- “What is your leadership style?” Instead, ask: “Tell me about a specific time your team was in conflict or performing poorly. What was the internal emotion you felt, and what specific step did you take to turn it around?”
- “What’s your biggest weakness?” Instead, ask: “What is a piece of feedback you’ve received that felt uncomfortable, but you knew was true?”
- “Why do you want this job?” Instead, ask: “Walk me through the exact moment you realised your last company was no longer the right place for you to grow”.
Candidates who are experienced sometimes prepare polished answers, so here is what to do in that case.
When a candidate gives a high-level, polished answer, use follow-up questions to force them to step away from their script and recall the raw experience.
- What were the exact words the client used when they were angry?
- What was the biggest risk you were afraid of taking in that scenario?
- If you had to redo that project, what specific tool or method would you change?
Real memories take a moment to recall, whereas memorised answers come instantly, so a pause and think between questions and answers indicates that an applicant is giving the question time and thought.
Are you looking for an “AI software engineer”? Then, do keep in mind the smart interviewing process.
Author Bio:
Akshay Sharma is a social media marketing enthusiast and has written many topics in the related field. He loves to write and read about latest technology trends. Currently, He works for Transparent Tech, a famous AI software engineer in the UK.
