Interviewing AI-Assisted Developers: Practical Strategies for Assessing Modern Coding Skills

The advent of AI coding tools has significantly altered the development landscape, influencing everything from rapid prototyping to debugging processes. For those involved in recruitment, a key challenge now is effectively evaluating a developer's proficiency in harnessing these AI assistants during interviews.

Evaluating AI Tool Interaction and Thinking Strategy

One effective approach centers on understanding the developer's thinking strategy and their specific interaction patterns with AI tools. This involves a closer look at:

Prompt Quality and Optimization: Assessing their ability to craft clear, effective prompts and refine them for better outputs.
Context Engineering: How well they provide relevant context to the AI, and conversely, how they "de-pollute" the context to avoid irrelevant information.
Strategic Application: Beyond just getting working code, evaluating the strategic application of AI as an enhancement to problem-solving, rather than just a code generator.

Prioritizing Learnability and Adaptability

Given the rapid evolution of AI technology, specific experience with a particular LLM might quickly become outdated. A more enduring strategy is to seek out candidates who are inherently fast learners and adaptable. Look for individuals who:

Integrate New Tools: Demonstrate a proven ability to quickly learn and integrate novel tools into their existing workflow.
Self-Assess Impact: Can honestly articulate how AI tools have changed their day-to-day work and potentially their team's dynamics.
Mentor and Evaluate: Show a propensity to evaluate new tools, write guides, conduct training, or mentor others, indicating a broader contribution beyond their individual output.

Practical Assessment: Live Coding and Observation

For a hands-on assessment, consider a time-boxed coding challenge, such as building a simple component within an hour. Crucially, allow candidates to use any resources they wish, including AI assistants. The evaluation then expands beyond just the final code to include:

Communication: How they break down the problem and articulate their approach.
Code Quality: The cleanliness, maintainability, and efficiency of the generated code.
Final Result: The functionality and completeness of the solution.

During such live sessions, observing the candidate's interaction with AI tools provides valuable insights. Pay attention to:

Prompting Techniques: Their method of querying the AI.
Review and Correction: How they critically review AI suggestions and iterate on outputs, demonstrating a strong understanding of the underlying problem and a nuanced approach to refinement.

Ultimately, the goal is to identify developers who not only produce effective results but also possess the critical thinking, adaptability, and strategic acumen to leverage advanced tools, continuously learn, and enhance their overall productivity.