What’s an AI Model? A Guide for Creators

What if you could multiply your creative output without multiplying your workload? With artificial intelligence (AI) models, that’s now a reality. AI is the tech behind time-saving tools like auto-editors, computer narrators, and script generators — including the ones built into Captions. Best of all, you don't need a background in tech to benefit from them.

However, knowing how they work can help you get more out of Captions. Read on to understand the difference between AI models and how to use them in your content.

What’s an AI Model?

AI models are computer programs trained to recognize data patterns and make decisions. These models power everything from search engines to creative tools that help you write, edit, and design.

The idea isn’t new. Early AI models, inspired by how the human brain works, were developed in the mid-20th century for tasks like responding to human moves in chess. However, recent breakthroughs in machine learning (ML) and deep learning make them far more capable and useful for content creators.

Today’s AI algorithms can do a wide range of content creation tasks, such as cloning your voice, generating visuals, and writing scripts. In platforms like Captions, AI tools are built directly into the video creation workflow. That means tasks happen in seconds, all thanks to the AI running in the background.

AI vs. Machine Learning vs. Deep Learning: Key Differences Explained

People often use these terms interchangeably, but they’re not the same. Here’s how they compare.

Artificial Intelligence

AI is the broadest concept and refers to any system that mimics human intelligence. This includes everything from rule-based programs that follow if-then logic to complex tools that can learn, reason, and create. AI allows tools to "think" and respond, not just follow a fixed script. That's what allows software to adapt to your content.

When Captions automatically generates subtitles or removes background noise, that's AI at work. It's making smart decisions based on your video content.

Machine Learning

Machine learning is a subset of AI. Instead of being programmed with fixed rules, ML models learn from data by identifying patterns and adjusting settings accordingly. The more data you give them, the better they get at making predictions or decisions. 

Unlike basic AI, you don’t have to outline every step — ML models improve by generalizing from training examples. For example, ML tools can recommend the best video title based on past performance and relevant keywords.

Deep Learning

Deep learning is a type of ML that uses layered algorithms called neural networks to process data. Modeled after the human brain, these systems can analyze complex inputs like images and video. They’re especially effective at working with unstructured data — like raw footage — and detecting subtle patterns that traditional ML tools might miss. For instance, Captions uses deep learning to identify your voice, clone it, and reproduce it.

Types of AI Models

AI models come in different forms, depending on how they learn and make decisions. As a content creator, understanding the basics can help you choose tools that match your workflow. Here are three types you might come across.

Supervised Learning

Supervised learning models learn from labeled data, meaning their creators show them examples with the correct answers. These models are great for tasks with clear input and output. Supervised learning can help with a task like auto-generating subtitles by learning how spoken words match written text.

Unsupervised Learning

Unsupervised learning doesn’t rely on labeled data. Instead, the model explores patterns and relationships in raw information without being told what to look for. For example, AI might sort your video clips by tone, lighting, or topic without you tagging anything, making it easier to organize and locate footage.

Reinforcement Learning

Reinforcement learning is a trial-and-error method where a model learns by testing different actions and getting feedback. If something works well, AI gets a "reward" — if it doesn’t, it gets a "penalty." 

Say a model learns the best time to place ads in a video by seeing how viewers react. If more people keep watching or click through, that’s positive feedback. However, if they skip the ad or stop watching, that’s a signal to try something else. Over time, the model figures out what works best by learning from those responses.

This kind of model can also make real-time decisions, such as automatically adjusting video quality settings or learning which video formats perform best across platforms.

How Are AI Models Trained?

Developers feed AI models information, test their understanding, and keep refining until they deliver the intended results. Here's how the process works behind the scenes.

Data Collection and Preparation

AI models need large amounts of data to learn from. Developers "clean" that data — images, text, or audio — by filtering out irrelevant information. Then, they label it where necessary to help the model understand what it's learning.

Model Selection

In this step, developers evaluate different types of models based on factors such as:

• Speed and performance
• Scalability (how well the model can handle more data or user requests)
• Accuracy
• The type of data the model will process

For instance, some models are optimized for text, while others are better suited for speech. Based on the task at hand, developers select the type of model that’s most likely to deliver the best results once trained.

Training the Model

Here, developers feed data to the ML model in multiple rounds, and it starts learning patterns — similar to studying with flashcards. Each training session helps it recognize patterns, reduce errors, and generalize from the data. It adjusts its internal settings (called weights) each time to get closer to the right answer.

For instance, a voiceover model might learn how different tones, speeds, and inflections affect how human-like a voice sounds.

Validation and Hyperparameter Tuning

Validation tests the model on new data it hasn’t seen before. This helps researchers check whether AI is learning in a generalizable way, not just memorizing the training data.

To improve the model's performance further, it goes through a process called “hyperparameter tuning” to adjust its settings. This prevents overfitting, where the model does great on training data but struggles with new inputs, and ensures a good balance between accuracy and flexibility.

Evaluation and Deployment

Once training is complete, developers test the model one final time to measure its performance. If it meets the standard, it can now integrate with real-world AI systems, like the ones inside Captions.

Popular Examples of AI Models in Content Creation

Here are a few popular AI models that you can use to produce short-form social media videos.

Google Veo 2

Veo 2 is a generative video model — a type of deep learning system trained on vast amounts of footage to understand motion, physics, and cinematic structure.

The model analyzes your text prompt and interprets the scene you're imagining. It then generates high-resolution video with realistic animation, lighting, and camera movement — as if a real-life crew filmed the scene.

Integrated into tools like Captions, Veo 2 helps turn ideas into cinematic short-form video content (like Instagram Reels and TikToks) without filming a single shot.

DALL-E 3

DALL-E 3 is a text-to-image diffusion model by OpenAI. The company's developers train it on millions of image-caption pairs to understand how words translate into visual elements. When you type a prompt, the model generates detailed images and artwork that match your description. 

You can use DALL-E 3 within Captions to bring ideas to life with just a few lines of text, such as watermarks, illustrations, or product images for marketing.

ElevenLabs

ElevenLabs uses an AI speech synthesis model trained through deep learning to generate lifelike voiceovers. It analyzes the nuances of tone, pacing, and emotion in real speech to produce natural-sounding audio in over 30 languages. ElevenLabs then converts text into spoken audio that mirrors human expression.

Access ElevenLabs directly within Captions to narrate videos or build custom voiceovers without stepping into a recording booth.

Model Your Short-Form Content With Captions AI

AI models are creative problem-solvers. They offer usable answers to everyday challenges: Time constraints, creator burnout, and uploading schedules. By understanding how AI models work, you can use them intentionally — not to replace your creativity, but to amplify it.

Captions brings multiple AI models to you in one platform. For example, the AI Video Editor speeds up post-production by automatically removing silences and filler words. You can also use the AI Voice Generator to add or clone a voice. Pair these with our integrated AI models, and Captions becomes an all-in-one tool for your short-form content. 

Create smarter with Captions.