My mom was an English teacher and an electronics tech later in her career. I explained to her how LLMs are requiring a lot of technical writing and documentation up front and devs that haven’t tried that approach or hate that can be quick to dismiss when the output is bad. Her reply, “Oh so the humanities do matter!’ Hire juniors with a left brain right brain mix.

We have to be careful not to anthropomorphize them but LLMs absolutely respond to nuanced word choice and definition of behavior that align with psychology (humanities). How to judge that in an interview? Maybe a “Write instructions for a robot to make a peanut butter and jelly sandwich” exercise. Make them type it. Prospects who did robotics club have an edge?

Can they touch type? I’ve seen experienced devs that chicken peck its painful. What happens when they have to write a stream of prompts, abort, and rephrase rapidly? Schools aren’t mandating typing and I see an increase (in my own home! I tried…) of feral child invented systems like caps lock on/off instead of shift with weird cross keyboard overhand reaches.

first you have to decide if you want juniors that are able to push tasks through and be guided by a senior, as the juniors won't understand what they are doing or why the AI is telling them to do it "wrong".

senior developers already know how to use AI tools effectively, and are often just as fast as AI, so they only get the benefits out of scaffolding.

really everything comes down to planning, and your success isn't going to come down to people using AI tools, it will come down to the people guiding the process, namely project managers, designers, and the architects and senior developers that will help realize the vision.

juniors that can push tasks to completion can only be valuable if they have proper guidance, otherwise you'll just be making spaghetti.

I've thought about this lately. In order to do that, you need to know where people typically stumble, and then create a rubric around that. Here are some things I'd look for:

- Ability to clearly define requirements up front (the equivalent mistake in coding interviews is to start by coding, rather than asking questions and understanding the problem + solution 100% before writing a single line of code). This might be the majority of the interview.

- Ability to anticipate where the LLM will make mistakes. See if they use perplexity/context7 for example. Relying solely on the LLM's training data is a mistake.

- A familiarity with how to parallelize work and when that's useful vs not. Do they understand how to use something like worktrees, multiple repos, or docker to split up the work?

- Uses tests (including end-to-end and visual testing)

- Can they actually deliver a working feature/product within a reasonable amount of time?

- Is the final result looking like AI slop, or is it actually performant, maintainable (by both humans and new context windows), well-designed, and follows best practices?

- Are they able to work effectively within a large codebase? (this depends on what stage you're in; if you're a larger company, this is important, but if you're a startup, you probably want the 0->1 type of interview)

- What sort of tools are they using? I'd give more weight if someone was using Claude Code, because that's just the best tool for the job. And if they're just doing the trendy thing like using Claude Agents, I'd subtract points.

- How efficient did they use the AI? Did they just churn through tokens? Did they use the right model given the task complexity?