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 data (which is sequential, time-ordered data), helping to improve predictive accuracy and enhancing forecast reliability.
Healthcare and life sciences: Generating high-resolution medical imagery, such as MRIs or CT scans, for research, training, or improving diagnostic algorithms.
Public sector: Creating realistic synthetic data to test AI systems for governance, resource allocation, or policy impact analysis.
Energy and utilities: Simulating environmental scenarios for renewable energy planning and disaster preparedness. The use of diffusion models to simulate various energy scenarios, such as renewable integration and grid stability, can yield valuable insights for strategic planning.
Manufacturing and supply chain: Diffusion models generate innovative design prototypes, accelerating the product development cycle and fostering creativity.

Generative adversarial networks (GANs)

Generative adversarial networks (GANs) are widely-used generative AI models consisting of two main components: the generator and the discriminator. These two components work together to refine the model's performance.

Generator: Produces new data based on the training dataset in response to a given prompt.
Discriminator: Evaluates whether the generated output is real (consistent with the training data distribution) or fake (deviates from the distribution). It provides feedback to the generator, helping it improve over time.

The interplay between these components leads to adversarial training, where the generator improves its ability to produce realistic outputs while the discriminator refines its ability to distinguish between real and generated data. This makes GANs particularly suitable for several use cases, including:

Financial services: Synthesizing realistic customer transaction data for fraud detection models, or generating new market scenarios for stress testing.
Healthcare and life sciences: Creating simulated medical images for training diagnostic tools, such as X-ray or MRI scans, without risking patient confidentiality. These have then been used to train AI-driven diagnostic systems without using private patient data as training data.
Public sector: Creating synthetic datasets that preserve privacy, helping to develop and test public policies without compromising sensitive information.
Energy and utilities: Simulating weather patterns to model renewable energy production and grid resilience.
Manufacturing and supply chain: Creating realistic virtual prototypes for product design and stress testing in simulation environments.

Variational autoencoders (VAEs)

Variational autoencoders (VAEs) are another important type of generative AI model, and consist of an encoder and a decoder.

Encoder: Creates a simplified version of the data map, capturing the key features of the data.
Decoder: Reconstructs data from the data map to generate new outputs similar to the training data, yet uniquely distinct.

Unlike GANs, VAEs are probabilistic models—they learn from input data and then make educated guesses based on it, working with a simplified data map.

VAEs do not have a built-in mechanism like a discriminator to judge the accuracy of their responses. Instead, they learn to generate realistic data by reducing the gap between the original data and the new data they generate. This is done using a loss function, which helps the model perform better by measuring how different the generated output is from the original.

Use cases for VAEs include:

Financial services: Mapping financial data into latent spaces to identify patterns for portfolio optimization or anomaly detection.
Healthcare and life sciences: Generating synthetic genomic data to support research or create personalized treatment simulations. VAEs have also been used in drug discovery to generate novel molecular structures, allowing drug companies to explore potential candidates for new drugs more efficiently.
Public sector: Developing probabilistic models for disaster response scenarios or resource allocation optimization.
Energy and utilities: Modeling energy consumption patterns by encoding and decoding historical usage data.
Manufacturing and supply chain: Generating variations of design blueprints based on latent feature representations to optimize for cost and efficiency.

What are generative AI models?

Generative AI models are statistical models that have been trained on large datasets to uncover underlying patterns. They learn how data is structured and can then generate new content by predicting the next element in a sequence—such as the next word in a sentence.

However, generative AI models don’t stop at word generation. They encompass a wide range of subsets, each tailored to a specific task and using its own specialized method. For example, some models specialize in natural language generation, others in video creation, image synthesis, code production, or even scientific simulation.

Generative AI statistics

The explosive growth of generative AI is reshaping industries, creativity, and daily workflows at an unprecedented pace. The data behind the AI revolution shows who is adopting GenAI, key applications, and the economic impact, underscoring why AI is no longer a futuristic concept but a strategic imperative for businesses today.

Within the financial services industry, for example, the banking sector is projected to spend a whopping $85B USD on GenAI by 2030 (Statistica). Nearly two-thirds of financial institutions surveyed, including banks, capital markets, wealth management firms, and insurers, are actively implementing or testing GenAI for risk and compliance (Celent).

For public sector organizations, the value of GenAI will surge to $1.75T by 2033, with the government market for GenAI products projected to grow at a 50% annual rate (BCG). Ninety percent of organizations surveyed are planning to explore, pilot, or implement agentic AI solutions in the next 2-3 years (Capgemini).

And the energy sector is no different. AI is poised to transform the oil and gas value chain, with a potential 30-70% increase in EBIT (earnings before interest and taxes) over the next five years for companies that fully integrate agentic AI into their operations, thanks to streamlined workflows and autonomous decision-making (BCG).

How do generative artificial intelligence models work?

Generative AI models require initial training—typically on volumes of domain-specific data—before they can begin producing output. This involves leveraging deep learning architectures, such as transformer models (e.g., large-scale language models) or diffusion models (e.g., image generation models), to identify patterns and structures in the training data.

Once trained, a GenAI model can be fine-tuned to align with specific use cases. For example, you could fine-tune a model on government policy documents and public sector reports to generate responses related to public policy and administrative processes. But how does this work in practice?

The initial model training

Training generative AI foundation models involves several main steps.

Data input: The model is initially trained on a large dataset representative of the expected output. Depending on the task, this could include text, images, audio, or other data types. The GenAI models then turn the input data into mathematical representations for easier machine analysis.
Pattern recognition: As the model processes the training data, it identifies patterns and relationships within it. For instance, it might recognize how numerical values correlate in a dataset, such as trends in stock prices or the relationship between temperature and humidity in weather data.
Measuring accuracy: The model compares its output with the training data using a loss function (a sort of “error calculator”) to determine how close the output is to the expected result.
Model optimization: Using optimization algorithms, the model updates its parameters and minimizes the loss function. This is an iterative process that continues until the model produces accurate output.

Latent space representation

Generative AI models generally build a latent space, which is a simplified and compact summary of the most important features of the training data. Latent space is used in several ways:

Generation of new content: By sampling from the latent space, models can create new data points. For example, in image generation, navigating the latent space allows the model to blend different features and generate new images that still resemble the original ones.
Control and customization: Latent space can be manipulated to achieve specific outcomes. By adjusting specific dimensions of the latent representation, models can control attributes such as the style of an image, the tone of text, or the features of generated audio.
Clustering and organization: Similar data points are often grouped together in the latent space, enabling models to effectively categorize and generate variations of similar content.

Generative AI output

Once trained, generative AI models can then produce new data by using latent space to find links between data points in the training dataset. The model takes an input prompt—an image or piece of text, for example—and generates a response by making educated guesses about where each data point in the input has been mapped during the training process.

Improving the accuracy of generative AI

Typically, AI models measure responses against a set of predefined metrics to assess accuracy, or they may rely on human input to validate results and refine the model over time. The overall goal of this approach is to minimize the loss function on both training and validation datasets. The model's performance is then evaluated on specific tasks.

Some AI models, such as Generative Adversarial Networks (GANS), have a baked-in method for evaluating output accuracy. These models use a discriminator (an “accuracy checker” for AI responses) to decide how accurate a response is, and the generator iterates to improve the result until the discriminator no longer classifies it as fake.

Benefits of generative AI models

Generative AI models can provide a range of benefits, including the following.

Fostering innovation

Integrating generative AI into the content creation process can enhance workflow efficiency and streamline specific tasks, such as automating content generation or personalizing content campaigns. Multimodal generative AI can generate text, images, video, code, and more, significantly reducing the time spent on initial development stages.

Moreover, generative AI serves as a powerful tool for fostering innovation. For example, by enabling rapid prototyping with enterprise generative AI, designers can quickly explore ideas without dedicating extensive time to manual testing. This allows them to focus on refining and developing more unique and practical solutions that could better impact the market.

Streamlining routine tasks

Using enterprise generative AI models can significantly improve the efficiency of repetitive, manual tasks when integrated at the appropriate stages of operational processes. For instance, tasks such as data entry or dealing with customer requests over chat can be handled swiftly and effectively by GenAI models, reducing the need for extensive human involvement.

Deploying secure AI within the enterprise can lead to considerable productivity gains from time and cost savings, alleviating the burden of, say, frontline customer triage on employees. By automating routine tasks, staff can focus on more complex, strategic, and higher-value responsibilities, enhancing overall productivity and job satisfaction.

Data generated for applications like prototyping and medical research also offer enhanced security and privacy benefits. The topic of security and generative AI is a complex one, but there are many ways GenAI systems can protect private data and still allow access to the underlying insights contained within. For example, private data can be cleaned and sanitized to remove sensitive data and anonymized before it is used for outputs.

Delivering personalized experiences

Generative AI can greatly enhance consumers' online shopping journeys by presenting a much richer and more tailored user experience.

A good example of this would be virtual shopping assistants. These AI tools can provide potential customers with product recommendations and answer any questions they have about a specific product, essentially guiding them through their shopping journey.

Additionally, AI can be used to identify and prompt potential upsells and cross-sells that the customer may be interested in real time. The result is a shopping experience that is more immersive and informed for the customer and optimized for generating the highest volume of sales.

Supporting problem-solving

Generative AI models can help enterprises to create alternative solutions to well-defined problems. This can be a great benefit in certain sectors. For example, in finance and investing, AI can be used to predict the outcome of financial decisions based on different conditions. Logistics is another sector that could use AI-generated models to solve potential supply chain problems before they become real issues.

Domain-specific knowledge

Certain sectors, such as medicine and healthcare, are built upon a unique and intricate body of knowledge. Generative AI can be trained on specialized datasets that encompass this domain-specific expertise, which can then enable GenAI-driven systems to function as advanced and intelligent advisors.

General AI models—usually pre-trained on large, diverse datasets and not for any particular task or domain—can be fine-tuned on domain-specific data to make them suitable for specific tasks. Compared to training a model on specific domain knowledge from scratch, using a general model and fine-tuning it tends to be far more cost-effective.

This can result in providing solutions to complex problems at a pace significantly faster than traditional human capacity. In essence, it serves as an interactive encyclopedia tailored to a particular field, offering detailed and contextually relevant information to support decision-making and problem-solving.

Challenges of generative AI models

Generative AI models have the potential to revolutionize numerous industries. However, their development and deployment can come with challenges that you’ll need to address to ensure ethical, secure, and effective use.

High resource costs

Training and fine-tuning generative AI models can require significant computational resources, which can come at a relatively high cost.

A potential solution here is to develop more resource-efficient algorithms. Alternatively, you could leverage emerging and more methodical hardware options, such as specialized hardware like GPUs or cloud-based services, that achieve the same goal. Another option is to use a pre-trained model and then fine-tune it for specific use cases.

Data privacy issues

Generative AI models need to be trained on large datasets. These datasets may contain sensitive and personal information, which could lead to data privacy and compliance issues.

When training new models, anonymize any training data before introducing it to mitigate these data privacy issues.

Want to learn more about data privacy in AI?

Introducing bias

If training data is biased, the AI model might incorporate these biases—and given training data is largely based on human-created content, the risk of introducing unconscious human bias is real.

Tackling bias helps to not only achieve fairness, but also ensure better results. To mitigate this problem, data should be sanitized and curated to ensure that no bias exists prior to beginning model training.

Poor transparency

Generative AI models are complex mechanisms, and it can be hard to understand how a model produces a specific response. This lack of transparency can be a problem in decision-making.

To address this, explainable AI techniques can be used to map the way a model produces responses. Such techniques include using attention mechanisms (in NLP models) to highlight which parts of the input data the model is focusing on to make predictions; or working with surrogate models, which are simpler models that approximate the predictions of a more complex model and provide information about how a prediction is made.

How to choose the right model

Let’s now look at how you can pick the right model and avoid the pitfalls of generative AI projects. Choosing the right type of generative AI model for your needs involves numerous considerations, some of which are detailed below.

Know your data: Some AI models are better at handling specific types of data, so it’s important to understand what kind of data your model will be processing.
Define output goals: Define your desired output. For example, will your model mine data, generate images, or create text? Transformers are primarily used in machine learning tasks involving sequential or structured data. Diffusion models are designed for generative tasks, where the goal is to produce new data.
Check resource requirements: Every AI requires computing resources, such as processing power (e.g., GPUs and CPUs) or network bandwidth. Can your infrastructure support these needs? In most use cases, the cheapest route may be using a pre-trained model from an external provider.
Think about model training: Your AI model will need to be trained (unless you use an off-the-shelf model). If you’re training it yourself, do you have access to sufficient data and the expertise necessary to fine-tune the model?
Controlling results: Do you need control over the responses generated by the AI model? Some models are more versatile than others. Transformer and diffusion models are usually controlled through text prompts, while VAEs generally offer greater control over responses than GANs.

The future of generative AI models

Generative AI models are set to advance exponentially in the coming years. Growth is expected in a number of areas, with projected enhancements in their capabilities, as well as the various ways enterprises can leverage them. For example:

Greater capabilities: The technology behind generative AI models will continue to improve, refining their capabilities and increasing their effectiveness.
Improved personalization: Generative AI will likely become better at providing fully personalized responses, especially in use cases such as online shopping and customer support.
Better energy efficiency: Currently, the computing resources required to train and use generative AI models can be costly. In the future, more efficient architecture could be developed to reduce energy costs without negatively impacting performance.

Regardless of how generative AI models evolve, there’s little doubt that the technology is one for forward-thinking enterprises to keep in sight.

FAQs about generative AI models

What type of AI is generative AI?

Generative AI is a subset of artificial intelligence focused on creating new, original content—such as text, images, audio, or code—using machine learning models trained to learn and replicate patterns in existing data. Unlike discriminative AI, which classifies or analyzes inputs, generative AI produces novel outputs by leveraging architectures like neural networks, often through probabilistic or adversarial processes. Learn more about generative AI.

Are LLMs a type of generative AI?

Yes, large language models (LLMs) are a specialized form of generative AI designed for natural language processing. They generate human-like text by predicting sequential word probabilities, enabling applications like drafting articles, translating languages, or engaging in conversational dialogue. Learn more about LLMs.

What type of data is generative AI most suitable for?

Generative AI excels with high-dimensional, pattern-rich data, such as text, images, audio, and video, where it can learn underlying distributions to synthesize realistic outputs. It is particularly effective for creative tasks (e.g., art generation), data augmentation, and scenarios requiring diverse or personalized content, though it may struggle with highly structured or rule-bound datasets. Learn more about generating content with Cohere Command.

How do you build a generative AI model?

You build a generative AI model by first deciding on the appropriate type of model; transformer and diffusion models are popular choices. Next, you create and set up the structure of the neural network, which involves choosing how the system will learn by selecting the learning algorithm, loss functions, and how it will improve over time. Once the architecture is in place, train your model on a suitable dataset and then fine-tune it to optimize it.

How do you train a generative AI model?

Training generative AI models begins with getting together a large enough dataset for training purposes. The dataset needs to be sanitized and accurate, as it will form the knowledge base the AI model uses to create responses. During training, the model learns patterns and structures from this dataset. Once trained, the model typically undergoes fine-tuning to increase accuracy.

What is a model in generative AI?

In generative AI, the term "model" is used to describe the mathematical framework that helps the AI system learn. It’s a set of algorithms (i.e., rules) that guides the AI as it learns from a set of data, spots patterns and structures, and maps out a simpler version of that data. This representation helps the model in generating relevant data based on what it has learned.

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