About The CCAI Glossary

The CCAI Glossary is a set of cross-industry definitions developed and ratified by the people who make film, television, and content.

Our goal is to create a shared language for how we, as an industry, discuss, and make decisions about how (and if) we want to use AI in our work.

It is a living document that will be updated as the technology evolves, but we need your help! Each definition has a field to invite your feedback. If you are interested in becoming a Definitions Delegate, please sign up by clicking “Get Involved” on the main page.

Our Goal

Generative AI is here, and its impact is being felt unevenly across creative work. Without shared terms and comparable evidence, people are left to make sense of a new, complex technology directly impacting their industry all from headlines, vendor claims or isolated experience.

CCAI gives the creative community common reference points: definitions discussed and ratified across disciplines, derived from real-world, role-level lived experience and evidence about how AI technologies and capabilities modify work, and a way for creatives to test that evidence against what they are seeing themselves.

The goal is not to predict one future for the entire industry. It is to make complex, ongoing change understandable and visible. We can upgrade our industry's systems and institutions. When people can see what colleagues in their own roles are reporting — using (or not using) AI tools, discussing new technology and its capabilities and impacts, and identifying where education and cross-skilling may help — they are able to respond with clarity and agency. As further testimonies come in, the evidence becomes clearer and more useful to creatives, helping to turn fear and confusion into empowerment.

About CCAI

The Creators Coalition on AI is a tech-agnostic convening organization created as a central hub for cross-industry discussion about how AI is affecting entertainment and creative communities.

The wider coalition includes members of the DGA, SAG-AFTRA, WGA, Producers United, PGA, and IATSE, alongside independent artists, executives, and technologists. People participate as individuals in an open discussion—not as organizational endorsements. To see our founders and signatories, click here.

FINE-TUNING

The process of further training a pre-trained foundational model on a smaller, task-specific dataset to adapt its behavior for a defined application, domain, or style.

History & Rationale

Pre-Publication V2. The process of further training a pre-trained foundational model on a smaller, task-specific dataset to adapt its behavior for a defined application, domain, or style. Fine-tuning preserves the broad capabilities of the base model while improving performance on targeted tasks. Implementations range from full parameter updates to parameter-efficient methods such as LoRA. See also Custom Model: LoRA, Custom Model: Supervised Fine-Tune.

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Deliberation Summary:

Question on the relationship between this term and foundational model.

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Pre-Publication V1. The process of further training a pre-trained foundational model on a smaller, task-specific dataset to adapt its behavior for a defined application, domain, or style. Fine-tuning preserves the broad capabilities of the base model while improving performance on targeted tasks. Implementations range from full parameter updates to parameter-efficient methods such as LoRA. See also Custom Model: LoRA, Custom Model: Supervised Fine-Tune.

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FINE-TUNING

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