SkillGap Radar AI Web App Introduction скачать в хорошем качестве

SkillGap Radar AI Web App Introduction

SkillGap Radar integrates Google’s Gemini 3 Flash model as the core reasoning engine to perform semantic skill gap analysis between a Job Description and a Candidate Resume. Unlike traditional ATS tools that rely on keyword matching, this application leverages Gemini’s contextual understanding and long-context reasoning to infer implicit skills, assess competency depth, and generate explainable outcomes. The model is configured to return strictly structured JSON outputs using a predefined response schema. This ensures deterministic, machine-readable results that can be safely rendered into charts, tables, and reports without parsing errors. Gemini is instructed to act as a senior hiring evaluator: inferring hidden requirements (e.g., understanding that “microservices” implies Docker and container orchestration), assigning skill levels on a 1–5 scale, and explicitly identifying missing or weak competencies. The application supports two reasoning modes. A standard mode prioritizes speed for rapid evaluations, while an optional Thinking Mode (Chain-of-Thought) allocates a token budget for deeper internal reasoning before producing final results. This enables recruiter-like analysis rather than surface-level scoring. Gemini-generated outputs drive the entire experience: skill gap quantification, business risk indicators, and a personalized upskilling pathway. In SkillGap Radar, Gemini is not a chatbot—it is a structured decision-support system that transforms unstructured resumes and job descriptions into actionable, explainable insights.