AI Resume Analyzer

Job seekers submit generic resumes resulting in low response rates. Professional resume review services cost $100–500 and take 3–7 days. There was a need for instant, detailed, AI-powered resume feedback that was accessible and affordable.

Built a two-model pipeline: GPT-3.5 for structured extraction (sections, skills, keywords) and GPT-4 for qualitative analysis. Used structured prompts with defined scoring rubrics to reduce score variance from ±15 to ±3 points. Cached common job description keyword extraction to reduce LLM calls by 40%.

500+ analyses in first month at $0.04 per analysis with 85% user satisfaction

• Average analysis time: 18 seconds (target was 30)
• Per-analysis cost: $0.04 (down from $0.15 with naive approach)
• Score consistency: ±3 points variance (down from ±15)
• 500+ analyses completed in first month
• 85% of users rated feedback as helpful or very helpful
• 22 point average score improvement on resubmission

Tradeoffs and lessons

Structured prompts are non-negotiable for production LLM applications. Two-model pipelines save money without sacrificing quality. Users want specific feedback ('Add Kubernetes to skills') not generic advice ('Add more keywords'). Caching is essential for LLM cost management.