Most teams think it is simple: the engineer asks, the AI does the work, that is it. It is not.
Whether the AI ships a real fix or a confident-sounding regression depends on the engineer. EasyEnv scores that skill - prompt quality, critical review, mode awareness, responsible use - on a real Linux box, recorded end-to-end.
Live demo"They ask, AI does the work, that is it."
This is what most managers think when they see a candidate use Claude or ChatGPT in an interview. It treats AI as a one-shot answer machine and the engineer as a typist who forwards questions to it.
The output is only as good as the engineer behind it.
Prompt quality, mode awareness, critical review, and responsible use decide whether the model ships a real fix or a confident-sounding regression. AI literacy is the skill that separates the two.
Four dimensions decide whether an engineer ships with AI or just generates noise with it. Every EasyEnv interview measures all four against a real task.
Understands AI limitations, hallucinations, bias, and reliability. Knows when the model is bluffing, when its answer is plausible-but-wrong, and which problems it cannot reach at all.
Writes clear, structured prompts to get high-quality results. Gives context, constraints, examples, and a definition of done so the model produces a real answer instead of a guess.
Verifies outputs, spots mistakes, and applies human judgment. Runs the code, reads the diff, checks the tests, and pushes back when the model is confidently wrong.
Uses AI securely, ethically, and without exposing sensitive data. Knows what to never paste into a model, respects licensing and attribution, and stays inside the team's guardrails.
Pick the mode that matches the role. Score the candidate on how well they operate in it - and on whether they recognise which mode the task calls for.
Does not operate effectively in this mode at all.
Some capability but inconsistent. Needs heavy mentoring.
Reliably effective in this mode. Ready for the job.
Operates at a level you would want to learn from.
No spreadsheets, no honour system. The whole session is recorded, scored, and ready to replay.
Candidates get a fresh Linux VM with their stack and the AI of your choice (Claude, GPT, an open model on Ollama). Same model, every candidate, so the comparison is fair.
Every prompt, every model response, every accept or reject is captured and timestamped against the code change. Reviewers see the collaboration, not just the artifact.
Tag each task as AI-Free, AI-Assisted, or AI-Directed. The rubric and the dashboard adapt, so you compare candidates on the same axis.
Run an AI-literacy interview on EasyEnv in under 10 minutes. Real box, real model, recorded end-to-end.