C.I.T. Perspectives on A.I. - Dr. Leo Gonzalez Jr & Deija McCalla
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C.I.T. Perspectives on A.I. - Dr. Leo Gonzalez Jr & Deija McCalla
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In this episode of AI Productivity Workflow, we explore how the next generation of counselors views AI in education and clinical practice.
Deja McCullough – Template Generation for Clinical Use
Goal: Create reusable note templates for different client diagnoses.
Workflow Steps:
1. Start with an existing case note or simple structure you’ve already written.
2. Prompt the LLM (e.g., ChatGPT) with a request:
“Help me adjust this note into a generalizable template for clients with [diagnosis].”
3. Iterate interactively — refine the language through back-and-forth chat until it fits your clinical style.
4. Save the polished version into a master document (e.g., Google Docs) as a collection of templates.
5. Use and adapt these templates across clients (e.g., generalized anxiety, depression) as time-savers while maintaining clinical accuracy.
Core principle: Iterative collaboration between counselor and AI for structure, not substance.
Dr. Leo Gonzalez – Prompt Engineering and Peer-Review Workflow
Goal: Improve prompt quality and leverage AI for academic and evaluative tasks.
Workflow Steps:
1. Create a “Prompt Library” — store tested prompts categorized by use (teaching, research, writing).
2. Use AI to generate prompts by asking the model:
“Help me make a prompt for [specific task/topic].”
3. Test prompts interactively until results are consistent and useful.
4. Apply prompts to complex tasks, e.g., having the LLM act as a peer reviewer for a journal manuscript.
5. Evaluate model outputs for bias, tone, accuracy, and usefulness.
6. Compare different LLMs via lm-arena.ai, which blind-tests responses from multiple models to see which performs better.
Core principle: Use LLMs not just for answers, but to design better questions and refine workflows through self-generating prompts.
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