🚀 Resume Feedback Is Easy — Until You Try Making It Context-Aware
On March 21st, a developer published something that starts as a build log and slowly reveals itself as a reckoning. The project sounded familiar: a resume feedback tool, the kind that sits in the back of every AI-curious developer's idea notebook. Point a model at a resume, receive structured criticism, deploy before the weekend ends. Version one practically builds itself. Flag the weak verbs, note the missing metrics, suggest some keywords. The model handles it. The developer ships it. And then someone actually uses it — a mid-career product manager switching industries, say, or a recent graduate with three internships and no full-time history — and the feedback lands completely flat. Technically correct. Contextually useless. The system told the career-changer to quantify their achievements without understanding they were trying to cross a credibility gap, not polish an existing one. This is where the developer's post stops being a tutorial and becomes something more honest. The problem, it turns out, is not generating feedback. Any reasonably prompted model can do that. The problem is generating feedback that knows who it's talking to — what stage of career, what industry target, what kind of gap exists between where the person is and where they're trying to go. Context-awareness sounds like a feature. It is actually a different product.
If confirmed as a broader pattern, this matters beyond one developer's weekend project. Resume tools are one of the most-built AI applications right now, which means the gap between "generates plausible feedback" and "generates useful feedback" is being papered over at scale. Thousands of job seekers may be receiving advice that is syntactically sound and strategically wrong — optimised for a generic candidate profile that matches no one in particular. The second-order effect is subtler: developers building these tools face a prompt engineering problem that resolves into a product design problem. You cannot write your way to context-awareness with a clever system prompt. You need intake, you need user modeling, you need the discipline to ask questions before you give answers. That discipline is unglamorous and it adds friction, which is exactly why most tools skip it. The ones that don't may find they have a real differentiation, not just a feature.
Watch for other developers publishing similar post-mortems about AI feedback tools that collapsed under real-world user diversity — that pattern of shared frustration would signal a genuine gap in how the field is approaching this category of application.
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