Most engineers trying to build AI skills are accumulating material, not capability. Courses pile up, notebooks multiply, and twelve months later the actual competence gain is hard to point at. The missing ingredient is rarely content — it is guided practice: one experienced practitioner regularly inspecting your real work. Use this AI skills audit to test, point by point, whether your learning setup has the properties of mentored growth or the properties of an expensive hobby. Score yourself honestly; anything below eight out of twelve means your progress is running on luck.

The 12-Point AI Skills Audit

Part one: does anyone see your actual work?

• 1. A named reviewer exists. Can you write down the name of one person — not a forum, not a chatbot, one human who has shipped production ML — who looked at something you built in the last thirty days? If the line stays blank, every other answer is weakened.
• 2. Reviews cover artifacts, not summaries. Describing your pipeline to someone is theater. The test is whether they open your feature store schema, your evaluation harness, your retraining job, and comment on the thing itself.
• 3. Correction arrives within days. Feedback that lands a quarter after the mistake cannot prevent the habit from forming. Mentored setups close the gap between error and correction to a week or less; count your current gap in days and write the number down.
• 4. The same person returns. A different reviewer each time resets the relationship to zero. Compounding only starts when one guide carries memory of your last five mistakes into reading your next attempt. (This is also where mentorship differs from drive-by code review.)

Part two: is the direction coming from experience?

• 5. Your roadmap was pruned by someone senior. The default self-study path contains everything, which means it ranks nothing. An experienced practitioner crosses out half your plan and reorders the rest — if your plan has never lost an item to a senior’s pen, it has never been reviewed.
• 6. Dead ends get named in advance. Veterans recognize doomed directions early: the model architecture that will never serve under latency constraints, the labeling approach that will not survive scale. Tally how many times in the past year someone redirected you before you burned the month.
• 7. You are assigned work slightly past your level. Self-directed learners drift toward comfortable tasks. A guide deliberately sets the next exercise a notch beyond reach — your first drift monitor, your first incident writeup for a misbehaving model — and stays available while you struggle through it.
• 8. Operations is in your curriculum at all. Building a model is a fraction of the job; running one is the career. If nobody has pushed you toward monitoring, rollback plans, and cost control, your education is missing the half that employers pay for. Our overview of where AI is actually earning its keep in business decisions shows how much of that value depends on systems that keep working after launch day.

Part three: is the relationship structured to compound?

• 9. Sessions recur on a calendar. “Reach out anytime” is how guidance dies. A standing slot — even thirty minutes fortnightly — outperforms ad-hoc help because the deadline forces you to produce something reviewable.
• 10. Each session ends with an assignment. If your check-ins end with encouragement instead of a concrete next build, you have a cheerleader, not a mentor. The between-session work is the growth; the session merely inspects it.
• 11. Your mistakes are logged somewhere. Keep a running file of every correction received. Reread monthly. Clusters reveal your systematic weaknesses faster than any skills assessment, and they give your guide a map of what to probe next.
• 12. The goal is dated and falsifiable. “Get better at MLOps” cannot fail, so it cannot succeed. “Serve a monitored model with automated retraining by March” can. Guided growth needs a target the guide can steer against.

Scoring and What to Do About It

Eight or more boxes ticked: you are inside a genuine mentoring loop — protect it. Four to seven: you have fragments; the fastest fix is usually converting an occasional adviser into a recurring one by proposing a fixed cadence and bringing finished work. Three or fewer: stop buying courses and start locating a practitioner. They exist and many teach: Rajesh Kumar’s DevOps and MLOps practice is a working example — a principal architect with 15+ years behind him who takes on engineers for sustained one-on-one mentorship anywhere in the world, precisely because structured guidance from one person who has operated these systems beats another subscription.

Two cautions as you act on the audit. First, do not confuse volume of advice with quality of guidance; one practitioner who knows your work beats ten strangers answering in passing. Second, keep your own scanning habit — guided does not mean passive. Staying oriented on the AI developments worth tracking this year keeps your sessions spent on judgment rather than news, and if you are earlier in the journey, a gentler on-ramp like our tour of approachable AI courses for people without a technical background gives you raw material worth bringing to a reviewer.

The pattern underneath all twelve points is one sentence: skills grow at the speed of corrected practice. Material is everywhere and nearly free. Correction from someone who has done the work is scarce — and it is the scarcity that decides, a year from now, whether your AI skills are real.