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In Short: Fine-Tuning

👋 Welcome to In Short — your midweek dive into one key machine learning concept.

Think of it as a tiny tech vitamin: quick, digestible, and good for your clinical curiosity. Over the weekend, we’ll follow up with real-world use cases and how it all ties back to your work.

This week we explore the broad concept of Fine-Tuning (see what I did there?:) )

What is Fine-Tuning?

Fine-tuning is how a general-purpose AI model (called a foundation model) gets trained further on specific, relevant data so it performs better in a particular context—like law or healthcare.

Think of it like mentoring a trainee therapist. They’ve got a solid grasp of theory, but now needs to adapt their skills to work with, say, adolescents, trauma survivors, or couples. The base skills are there, fine-tuning helps them adapt to the context and nuances.

How Does Fine-Tuning Work?

Fine-tuning happens after a model has already learned from broad, general data (like books, websites, or forums). AI engineers then retrain it on smaller, more focused datasets—like therapy transcripts, clinical notes, or culturally specific dialogues.

This process adjusts the model’s internal parameters (the knobs that help it “decide” what matters) to better reflect the tone, structure, and vocabulary of a particular field—say, therapy language or cultural idioms in mental health care.

Example? Fine-tune a model on CBT session transcripts, and it gets sharper at generating CBT-style reflections, summaries, or even client worksheets.

Who’s Doing the Fine-Tuning?

This is usually the job of machine learning engineers or AI researchers. However increasingly, AI product teams include domain experts who help define the data and evaluate results.

Which means folks who understand both AI and the clinical world are in high demand for fine-tuning foundation models for psychology and mental health.

Big players like OpenAI, Meta, and Google offer fine-tuning tools, and platforms like Hugging Face make it easier for smaller teams to join in. Most of the time, it’s done using frameworks like PyTorch or TensorFlow—though you’ll still need clean, labeled data and a steady hand on the tech.

TL;DR:

Fine-tuning is how a generalist AI becomes a domain specialist. It’s the behind-the-scenes step that helps models “speak therapy” or act in ways that feel more attuned, relevant, and ethical.

Even if therapists don’t do the fine-tuning themselves, knowing it’s part of the process helps explain why some tools feel more thoughtful—it’s not magic, it’s meticulous tuning with clinical data.

Thanks for tuning into In Short!
If this made AI a little clearer, feel free to share with a colleague who’s eager to learn but not a fan of tech-speak.

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See you this weekend,
Harshali
Founder, TinT