Generative Ai Models

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Dec 30, 2024

15 minutes read

A complete guide to the different types of generative AI models

Generative AI models use algorithms, such as neural networks, to ID patterns in existing content and use that information to create new and original content.

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_Updated: November 18, 2025_

It’s hard to believe the concept of artificial intelligence (AI) was first put forward in 1956; it can feel futuristic even now, well into the 21st century. And while innovation was slow at first, it’s picked up pace in the last decade. Generative AI models are one of the latest—and most exciting—developments in the AI field.

Computers can now generate content that closely mimics natural, human-created work, helping to increase productivity and taking over tasks traditionally reliant on manual effort. This can have a transformative impact on businesses large and small, freeing up the workforce to focus on the more strategic and creative tasks that are essential to growth. How did we get here—and where are we going? Let’s see how generative AI models are built, trained, and used by enterprises around the world.

##### What are the different types of generative AI models?

Generative AI models encompass diverse architectures and excel in specific data types and tasks. Each architecture balances trade-offs in quality, flexibility, and computational efficiency, driving innovation across industries. Currently, there are four main types of generative AI models that are being used by enterprises.

• Transformers dominate natural language processing via self-attention mechanisms, enabling context-aware text and code generation
• Diffusion models reverse noise patterns to synthesize high-quality data and media
• Generative Adversarial Networks (GANs) use competitive generator-discriminator dynamics to produce realistic outputs
• Variational Autoencoders (VAEs) merge compression and probabilistic sampling for creative data reconstruction, balancing fidelity and diversity

##### Understanding how to use different types of generative AI models

Generative AI models are revolutionizing industries by creating diverse, high-quality content across text, visuals, and audio. From crafting personalized marketing copy with Transformers to designing synthetic datasets via GANs, these architectures address unique challenges in creativity and efficiency. Understanding their specialized strengths unlocks transformative applications across healthcare, entertainment, and beyond.

###### Transformers

Transformers are foundational to many generative AI applications as they can process sequential data and understand complex dependencies. These models rely on attention mechanisms to produce coherent and contextually-relevant outputs:

• Self-attention mechanism: Transformers use self-attention to understand which parts of the data are most important, helping them create high-quality content.
• Embeddings: By using embeddings, transformers can understand and generate outputs that reflect the deeper meaning or context of the data.

Use cases for transformers include:

• Financial services: Automating the generation of financial reports, summaries, and investment insights. Transformers can be employed to predict economic indicators such as inflation and market volatility by analyzing historical and real-time financial data.
• Healthcare and life sciences: Helping to create and maintain medical documentation, summarizing patient histories, creating specialized reports, and generating research-focused content.
• Public sector: Generating natural language responses for citizen services or summarizing policy documents. Transformers can assist in policy drafting by identifying errors and biases, thereby supporting more informed and equitable decision-making processes.
• Energy and utilities: Optimizing energy management reports, predictive maintenance alerts, and analyzing sensor data. Energy companies can use transformer-based models to analyze unstructured data, such as technical documents and maintenance logs, to extract insights and inform operational strategies.
• Manufacturing and supply chain: Creating detailed, automated operational manuals or generating specifications for component designs. By analyzing time-series data from machinery, transformers predict equipment failures before they occur, reducing downtime and maintenance costs.

Learn more about Cohere’s transformer models, Command and Aya.

###### Diffusion models

Diffusion models are becoming more popular because of their unique, step-by-step approach to creating data. This means the model gradually improves its results until it achieves high-quality outputs.

• Noise addition and removal: Diffusion models start with noisy data—or data that is “unclear” and “messy”—and then work to gradually remove the noise by refining the data step by step. As the model works through this process, it becomes better at understanding the data and produces more realistic outputs.
• Latent diffusion models: These models work in a compressed latent space, which is a simplified map of data points. This reduces computational cost and improves efficiency while preserving the quality of the generated output.

Use cases for diffusion models include:

• Financial services: Simulating diverse market conditions for risk assessment or stress testing. Diffusion models can [remove noise from financial time series...