The introduction of AI tools such as ChatGPT, Copilot, and Codex has revolutionized coding, making it easier than ever to write code efficiently. However, as these tools become more ingrained in day-to-day development, they also have significant implications for technical interviews. Specifically, how should companies adapt their interview processes to account for the use of AI during live coding interviews?
In this article, we will explore the challenges and opportunities presented by AI interview tools, the role of mock interviews in preparing candidates, and how companies can evolve their interview preparation processes to adapt to this new reality. We are not suggesting immediate changes but instead reflecting on the potential impact of AI in interviews and what actions might need to be taken.
The Current Landscape
At many tech companies, such as OLX, internal AI tools are already used to boost productivity once a candidate is hired. For instance, OLX promotes the use of an internal LLM tool called PlusOne to assist in coding, coaching, and more. This raises an important question: Why do we not evaluate candidates during interviews on their ability to effectively use these AI tools if they will be using them daily once hired?
If we don’t address this gap, candidates who are unable to adapt to these tools may struggle to keep up with their peers. Conversely, we could be missing out on identifying candidates who excel at using AI to enhance their productivity. Mock interviews that integrate AI tools can help bridge this gap, ensuring that candidates are better prepared for the demands of the modern tech workplace.
The Role of AI in Coding Interviews
AI interview tools such as ChatGPT, Copilot, and Codex have introduced new complexities into live coding interviews. For those unfamiliar, ChatGPT is a large language model that can generate code when given a problem statement and programming language. Similarly, GitHub Copilot can autocomplete significant portions of code, and Codex is a powerful LLM from Microsoft designed to assist with coding tasks.
Recently, we observed candidates using Copilot and Codex during live coding interviews. Although we anticipated this possibility, we weren’t fully prepared to evaluate candidates who used AI tools compared to those who didn’t. This presents new challenges:
Should candidates be allowed to use AI tools during live coding interviews?
If so, how do we fairly evaluate their performance?
If not, how do we assess their ability to leverage these tools effectively in a real-world scenario?
Mock interviews that incorporate AI tools can serve as a valuable tool in this evolving landscape, allowing both candidates and interviewers to navigate these complexities before they arise in a real interview setting.
The Risks of Inaction
Failing to address these questions could lead to several issues:
Missed Potential: Candidates might not demonstrate their full potential if they are not allowed to use AI tools they are accustomed to.
Skill Gaps: We may fail to identify candidates who excel at using AI tools or struggle to adapt to them, impacting long-term performance.
Limited Assessment: Without AI tools, interviews may focus more on mundane tasks such as setting up code or managing dependencies, leaving less time to assess critical thinking skills.
Quality of Hires: The quality of hires could suffer if we do not adapt our interview processes to reflect the new normal in tech development.
By integrating AI tools into mock interviews, we can ensure that candidates are evaluated on both their traditional coding skills and their ability to effectively leverage AI tools. This will help mitigate the risks of inaction and better prepare both candidates and interviewers for the future of tech hiring.
Key Concepts in AI-Assisted Interviews
As we transition from traditional coding interviews to AI-assisted ones, several key concepts will become critical in evaluating candidates:
• Prompt Engineering: Crafting problem statements that AI tools can understand and respond to effectively.
• Code Review: Evaluating AI-generated code to identify edge cases and ensure it works as intended.
• Refactoring: Modifying AI-generated code to improve its architecture and maintainability.
• Debugging: Diagnosing and fixing issues in AI-generated code.
• Testing: Validating that AI-generated code works across various scenarios.
These concepts will help distinguish junior, mid-level, and senior candidates during interviews. For example, a senior engineer may be more adept at identifying edge cases or explaining the engineering choices made by the AI tool.
Mock interviews that focus on these key concepts can help candidates refine their skills and better prepare them for the demands of AI-assisted coding interviews.
The Future of Tech Interviews
Live coding interviews have long been a staple of tech hiring, providing a controlled environment to assess a candidate’s real-world performance. However, the rise of AI interview tools presents new challenges and opportunities for companies. Ideally, the future of tech interviews will involve a more collaborative approach, incorporating AI tools and team-based problem-solving to mirror a typical workday.
However, this approach comes with its own challenges. Interviewing already requires a significant time investment from senior engineers, and incorporating AI tools could add to this burden. As we move forward, companies will need to find ways to balance these competing demands.
In the meantime, mock interviews that integrate AI tools can serve as a valuable bridge, helping both candidates and interviewers adapt to the new normal in tech hiring.
Conclusion
AI tools such as ChatGPT, Copilot, and Codex are changing the landscape of coding and, by extension, coding interviews. While AI won’t replace core technical skills, it will add new layers of complexity to the interview process. Companies that start preparing now, through AI-integrated mock interviews and updated evaluation criteria, will be better positioned to identify top talent in this evolving landscape.
As we continue to explore the impact of AI on tech interviews, it’s crucial to stay ahead of the curve. By embracing these changes and adapting our interview processes, we can ensure that we are hiring candidates who are not only skilled coders but also adept at leveraging AI tools to enhance their productivity and performance.