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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