Manually screening 137 resumes took two full days; a system built on TraeWork (ByteDance's AI coding assistant capable of chaining multi-step tasks) ran all 6 test cases in 1 minute 36 seconds—but the most valuable finding from this real-world test isn't speed. It's that "the scoring criteria moved from inside my head onto the scorecard."
What This Is
The author broke initial resume screening into 4 steps: first, convert the JD (job description) into a scorecard with tiered scoring rules; then craft 6 trap-laden simulated resumes as an exam to verify whether the AI is reliable; once it passes, use AI to batch-process real resumes and produce an auditable ledger; finally, generate tailored interview questions and review scripts for the "pending" tier. Four commands issued in sequence within a single conversation.
Key mechanism: every conclusion must cite the resume's original text; if the AI cannot judge, it writes "questionable" and is forbidden from speculating. The simulated exam step is the soul of this whole method—the author explicitly states that real resumes have no ground truth, and deploying AI on real data without prior validation is gambling.
Industry View
Supporters see this as a textbook pattern for Agent (AI that autonomously plans multi-step operations) landing in production: break fuzzy human judgment into rules, hand them to AI for execution, and let humans only handle the final "pending review." The shift from "inside my head" to "on the scorecard" is essentially turning black-box judgment into an auditable process.
The opposing view is worth hearing too. First, 137 resumes is just the volume of a single position at a mid-sized company; when big-tech HR handles tens of thousands per year, the AI's 1:36 output advantage gets eaten up by prompt engineering (writing AI instructions) overhead—the author himself spent well over 20 minutes designing the exam. Second, the "scorecard" looks objective but actually solidifies HR's subjective standards: biases get automated right along with everything else. Whatever you filter out this year, you'll keep filtering out next year. Third, the job-seeker side is quietly shifting: when everyone writes resumes to match "proficient SQL + 3 years experience," the "unusual background" that HR actually wants to see gets filtered out by the system.
Impact on Regular People
For enterprise IT and HR: what's reusable isn't any specific tool—it's the "scorecard + simulated exam" methodology. Next time you want AI to take over any judgment-heavy task, first produce an exam you already know the answers to. Far safer than going straight to real data.
For individual careers: if you're job hunting, you need to be aware that HR's initial screen may already be done by AI. "Expert / Proficient / Familiar" on resumes will be processed as tiered categories, and skill claims must be backed by actual work content—otherwise they go straight into "questionable" or even "below tier."
For the consumer market: impact is currently limited, but the same method (validate before deploying) is migrating to contract review, customer service tickets, content moderation, and other scenarios. The "AI-ification" of judgment work will happen more quietly than generative work did.