# Google engineers are admitting their own HR filters can't be > Source: > Published: 2026-08-11 02:44:51+00:00 # Google engineers are admitting their own HR filters can't be If you are currently optimizing your resume for an ATS (Applicant Tracking System), you are essentially playing a guessing game with a black box that might be hallucinating or miscategorizing your experience. This isn't just about "keyword stuffing" anymore; it's about the fact that the underlying logic used to parse professional history can be inconsistent. To actually get past these filters and get your resume in front of a human, you need a more strategic AI workflow. Here is a practical tutorial on how to bypass the "algorithmic lottery" by using prompt engineering to align your profile with what the machine is actually looking for. ## How to audit your resume against an LLM filter 1. **Extract the core intent.** Don't just copy the job description. Feed the JD into a model and ask it to identify the "hidden" requirements—the specific technical competencies and soft skills that the recruiter is likely using as filter weights. 2. **Run a gap analysis.** Use a prompt like the one below to see where your current resume fails to trigger those weights. ``` Act as a technical recruiter at a FAANG company. I will provide a job description and my resume. Analyze the resume and identify exactly which required skills or keywords are missing or insufficiently emphasized. Provide a "match percentage" and a list of specific phrases that the ATS is likely searching for but cannot find in my text. ``` 3. **Rewrite for clarity, not just keywords.** Instead of just adding words, rephrase your achievements to match the semantic patterns of the industry. If the JD asks for "deployment experience," don't just say "deployed apps"; say "managed end-to-end deployment cycles for high-traffic microservices." 4. **Verify with a "blind test."** Paste your revised resume and the JD into a fresh chat session and ask the AI to "reject this candidate based on the JD." If it finds a reason to reject you, that is exactly where the HR filter will trip you up. This situation proves that a hands-on guide to prompt engineering is now a requirement for job seekers, not just a luxury. You can't rely on the system to be fair or accurate. The only way to win is to treat the application process like a deployment problem—test your input, identify the failure points, and iterate until the output (the interview invite) is achieved. [AI companies are living on investor hype instead of actual 12h ago](/en/news/5793/) [Google AI is basically just playing a game of probability 1d ago](/en/news/5703/) [Why is Google killing off Earth Pro on desktop by 2027? 1d ago](/en/news/5658/) [Since the original content provided was just a title 2d ago](/en/news/5594/) [YouTube's AI detection is getting way too aggressive for its own 3d ago](/en/news/5491/) [Demis Hassabis stepping back from DeepMind signals a weird shift 3d ago](/en/news/5430/) [Next AI Pulse adds a fake LED strip to the macOS Dock for agent status →](/en/news/5863/)