Talent Visa Evaluator: AI Workflow for Visa Assessment

A developer has built an open-source AI workflow called Talent Visa Evaluator that assesses applicants for the UK Global Talent Visa using a seven-phase process. The tool, built with Next.js 16, React 19, and Tailwind CSS v4, is provider-agnostic and works with Claude 3.5 Sonnet, GPT-4o, or DeepSeek. The system prompt forces the AI to act as a former assessor who recognizes equivalent evidence, avoiding rigid requirements like PhDs or specific awards.

Talent Visa Evaluator: AI Workflow for Visa Assessment The core logic isn't just a basic Q&A; it's a seven-phase AI workflow. It profiles the user's career stage first to determine if they fit the "Exceptional Talent" or "Exceptional Promise" route, then applies field-specific scoring tech, arts, academia, etc. to identify evidence gaps. The technical stack is pretty lean: Next.js 16, React 19, and Tailwind CSS v4. I built it to be provider-agnostic, so it works with Claude /en/tags/claude/ 3.5 Sonnet, GPT-4o, or DeepSeek via a simple env variable. The Prompt Engineering Strategy The real challenge was stopping the AI from being too rigid. Standard LLMs often hallucinate that a PhD or a specific award is a hard requirement. To fix this, I developed a system prompt that forces the AI to act as a former assessor who recognizes "equivalent evidence." Here is the logic structure I used for the field-specific system prompts: Act as a Senior Endorsement Assessor for the Global Talent Visa. Your goal is to provide a balanced, evidence-based evaluation of the applicant's profile. CRITICAL GUIDELINES: 1. Do not treat PhDs, patents, or mainstream media coverage as mandatory requirements unless explicitly stated for the specific field. 2. Look for "equivalent evidence"—e.g., a high-impact open-source contribution can outweigh a formal award in digital technology. 3. Avoid double-penalizing: if an applicant lacks a specific credential but proves the skill through work history, do not deduct points twice. 4. Structure the output into: Strengths , Critical Gaps , and Actionable Recommendations . 5. Maintain a professional, objective, and critical yet encouraging tone. By using a shared prompt builder, I can scale this to new professional fields without rewriting the entire evaluation framework from scratch. It ensures the AI stays consistent regardless of whether it's evaluating a software engineer or a fashion designer. Since it's open source, the goal is to let other devs refine the evaluation methodology. It's a practical tutorial in how to turn complex, bureaucratic guidelines into a structured LLM agent. The full project is available here: https://github.com/itamelions/talent-visa-evaluator Next Git refs can actually solve the coordination chaos that happens → /en/threads/3373/