Learners will develop a standalone interface to allow clinicians to leverage Chat-GPT to complete a labour market analysis.

A labour market analysis identifies current job opportunities, wage expectations, and employment trends for a specific occupation in a defined geographic area. It typically includes:

• Job titles and NOC codes
• Local job availability and employment outlook
• Wage ranges (entry, median, high)
• Required education, training, or experience
• Examples of current job postings

This analysis helps align a client’s skills and goals with realistic employment options.

🔧 1. Functional Web Interface

A working web application that:

• Presents the form fields from the Labour Market Analysis Request.
• Guides users through structured inputs (text boxes, checkboxes, dropdowns).
• Validates required fields and input formats.
• Summarizes user responses before submission to ChatGPT.

Technologies may include:

• Frontend: HTML/CSS, JavaScript, React or Vue
• Backend (optional): Node.js, Flask, or Django
• ChatGPT Integration: OpenAI API (via a backend or directly using JavaScript SDK if permitted)

🤖 2. Integration with ChatGPT (OpenAI API)

• Backend system that packages the form responses into a clear, structured prompt to send to the GPT model.
• Ensures prompt quality: for example, formatting client location, occupation, and selected options into a well-structured instruction to GPT.
• Returns and displays the GPT-generated labour market analysis in a readable format (e.g., text summary, downloadable report, formatted HTML).

📄 3. Prompt Engineering Strategy Document

• Description of how the prompt is constructed from user input.
• Explanation of how different user selections (e.g., remote vs in-person work, region, preferred data) influence the request to GPT.
• Strategy for ensuring consistent, accurate, and relevant responses.

🧪 4. Testing and Evaluation

• User testing with sample clinicians or peers to gather feedback.
• Sample outputs: Example searches run through the tool with comparisons to manually completed reports.
• Documentation of improvements made based on feedback.

🔒 5. Privacy & Ethical Considerations

• Ensure the interface does not collect identifiable client information or stores it without consent.
• If needed, include a disclaimer that the tool provides informational summaries only and does not replace regulated professional judgment.

🗂️ 6. Final Deliverables Package

• Source code (GitHub repo or ZIP)
• Setup instructions or deployment guide
• User guide for clinicians (1–2 pages)
• Developer documentation (API use, prompt formatting)
• Sample outputs and demo video (optional but valuable)

✅ Optional Bonus Features

• Ability to export the report (PDF or Word format)
• Multi-language interface (English/French)
• Admin dashboard to review usage or submissions
• Client-side saving of previously submitted forms