What Is Generative AI? A Guide for IT Leaders

How Does Generative AI Work?

At its core, every generative AI system uses a neural network trained on massive datasets. These networks learn statistical patterns and relationships within the training data, then apply this knowledge to generate new content that answers a user’s question. Think of it like learning a language—after exposure to enough examples, the system understands structure well enough to create something new.

The process involves three essential elements:

• Model architecture: The neural network structure that processes information
• Training data: The information the model learns from
• Inference engine: The system that generates new content based on learned patterns

‍

Key Technologies: Large Language Models, Diffusion Models, and GANs

• Large language models (LLMs) power text generation by predicting the most likely next word in a sequence based on context. ChatGPT exemplifies this approach, built on transformer architectures that process text in both directions simultaneously. This enables the models to understand complex relationships between words across long passages.‍
• Diffusion models create images by learning to reverse a gradual noise-addition process. During training, the model progressively adds noise to images until they become pure static, then learns to reverse this process step by step. At inference time, it starts with random noise and systematically removes it according to a text prompt, revealing a coherent image.‍
• Generative Adversarial Networks (GANs) employ two neural networks in competition. A generator creates synthetic data while a discriminator attempts to distinguish real from generated content. This adversarial training pushes both networks to improve until the generator produces highly realistic outputs.

Training Data Requirements and Model Architecture

Generative models demand unprecedented volumes of high-quality, diverse training data. LLMs typically train on hundreds of billions to trillions of tokens (such as LLaMA's 1.4 trillion tokens), individual pieces of text like words or subwords drawn from books, websites, code repositories, and other text sources.

The transformer architecture, introduced in 2017, revolutionized generative AI by enabling models to process entire sequences simultaneously rather than one element at a time. This parallel processing capability, combined with attention mechanisms that help models focus on relevant information, allows transformers to capture long-range dependencies and complex patterns.

Data quality matters as much as quantity. Models trained on biased, incomplete, or inaccurate data will reproduce and amplify those flaws in their outputs.

‍

Types of Generative AI Models and Their Applications

Large Language Models (LLMs) for Text Generation

LLMs excel at understanding and generating human language across a wide range of tasks. These models can draft technical documentation, summarize lengthy reports, answer questions based on context, and generate code from natural language descriptions.

In enterprise settings, LLMs automate routine writing tasks like email responses, meeting summaries, and status reports. They assist software developers by suggesting code completions, explaining complex functions, and generating test cases. Customer service organizations deploy LLMs to handle common inquiries, freeing human agents to address more complex issues.

The technology's versatility extends to:

• Translation: Converting text between languages while preserving meaning and context
• Sentiment analysis: Detecting emotional tone in customer feedback or social media
• Content moderation: Identifying inappropriate or harmful content at scale

However, LLMs can produce plausible-sounding but incorrect information (a phenomenon known as hallucination, with 2024 rates reported as high as 28.6% for GPT-4), requiring human oversight for critical applications.

Computer Vision Models for Image and Video Creation

Diffusion models and GANs generate photorealistic images from text descriptions, enabling designers to prototype visual concepts rapidly. These models can create variations of existing images, extend images beyond their original boundaries, and generate entirely synthetic faces or scenes.

Video generation builds on image synthesis by maintaining consistency across frames. Models learn temporal relationships to produce smooth motion and coherent sequences. Applications range from creating marketing materials to generating synthetic training data for computer vision systems.

Multimodal AI Systems for Cross-Format Content

Multimodal models process and generate content across different formats simultaneously. These systems can generate images from text descriptions, create captions for images, answer questions about videos, or produce diagrams based on technical specifications.

Recent advances allow models to understand context across text, images, and audio within a single system. A multimodal assistant can analyze a chart, read accompanying text, and provide insights that synthesize information from both sources.

‍

Generative AI Use Cases for Enterprise IT

Content Creation and Documentation Automation

Generative AI accelerates the production of technical documentation, user guides, and internal knowledge bases. Models trained on an organization's existing documentation can generate new content that matches established style and terminology. This capability reduces the time required to document new features, update procedures, and create training materials.

The technology also assists in maintaining documentation consistency across large organizations. Models can identify gaps in existing documentation, suggest updates based on code changes, and generate first drafts of API documentation from code comments.

Code Generation and Software Development

AI-powered coding assistants suggest code completions, generate entire functions from natural language descriptions, and explain complex code segments. These tools integrate into development environments, providing real-time assistance as developers write code.

Beyond simple code completion, generative AI can:

• Translate code: Convert implementations between programming languages
• Generate tests: Create unit tests automatically from function signatures
• Identify bugs: Spot potential issues in code before deployment

Developers still need to understand the generated code, verify its correctness, and ensure it meets security and performance requirements.

Data Augmentation and Synthetic Dataset Creation

Training machine learning models requires large, diverse datasets that may not always be available or practical to collect. Generative AI creates synthetic training data that preserves statistical properties of real data while avoiding privacy concerns.

In healthcare, generative models produce synthetic medical images for training diagnostic systems without exposing patient data. Financial institutions generate synthetic transaction data for fraud detection models that maintain realistic patterns without revealing sensitive customer information.

‍

The Generative AI Technology Stack for Enterprises

Infrastructure Requirements and Compute Resources

Training large generative models demands specialized hardware, particularly GPUs designed for parallel processing of matrix operations. Inference—using trained models to generate content—also requires substantial compute power, though less than training.

Network infrastructure becomes critical when training is distributed across multiple machines. High-bandwidth, low-latency connections between compute nodes prevent bottlenecks that would otherwise slow training. Modern data centers employ high-speed networking to support distributed AI workloads.

Data Storage and Management Considerations

Generative AI projects generate and consume massive amounts of data throughout their lifecycle. Training datasets often measure in terabytes or petabytes, requiring storage systems that combine high capacity with fast access speeds.

• Model checkpoints—snapshots of model state during training—enable recovery from failures during training. These checkpoints can each consume hundreds of gigabytes, with dozens or hundreds saved during a single training run.‍
• Object storage provides a scalable foundation for these diverse requirements, offering the capacity to grow with data needs while maintaining performance. The S3 API has become the de facto standard for AI workloads, with support built into major machine learning frameworks and data processing tools.

Model Training and Deployment Platforms

MLOps platforms streamline the model development lifecycle, providing tools for experiment tracking, hyperparameter tuning, and model versioning. These platforms integrate with storage systems to manage datasets and artifacts, with compute resources for training, and with deployment infrastructure for serving models.

Open-source frameworks like PyTorch and TensorFlow provide the foundation for building and training models. Higher-level platforms add workflow orchestration, resource management, and collaboration features that enable teams to work efficiently.

‍

Data Infrastructure Challenges for Generative AI

Massive Storage Requirements for Training Data

Training datasets for large generative models routinely exceed tens of terabytes, requiring exascale data infrastructure designed for these demands. Organizations building domain-specific models must collect, clean, and store proprietary data, though typically at much smaller scales. This data comes from diverse sources: document repositories, databases, application logs, and external datasets.

Storage costs can quickly spiral without careful planning. Organizations need storage solutions that provide high capacity at a reasonable cost while maintaining the performance required for training workloads.

High-Performance Data Access Patterns

GPUs can process data far faster than traditional storage systems can deliver it. Storage systems must serve data at rates high enough to prevent GPUs from sitting idle—wasting expensive compute resources.

The access patterns during training differ from typical storage workloads. Training involves reading the same large dataset repeatedly, with each training epoch requiring a complete pass through the data. Random access patterns emerge as training systems shuffle data to prevent models from learning spurious patterns based on data ordering.

Multi-Cloud and Hybrid Deployment Considerations

Organizations increasingly adopt multi-cloud strategies to avoid vendor lock-in and leverage best-of-breed services. Generative AI workloads may train on one cloud platform, deploy to another, and access data stored on-premises, though hybrid infrastructure presents unique challenges. Generative AI workloads may train on one cloud platform, deploy to another, and access data stored on-premises.

Moving large datasets between clouds or between cloud and on-premises infrastructure consumes time and incurs egress charges. Organizations need strategies for data placement that balance performance, cost, and compliance requirements.

Data Security and Compliance Requirements

Generative AI training data often includes sensitive information: customer records, proprietary documents, financial data, or personal information. Organizations must protect this data from unauthorized access while enabling legitimate use for model training.

Regulatory frameworks like GDPR, HIPAA, and industry-specific requirements impose constraints on how organizations collect, store, and use data for AI. Some regulations require that individuals can request deletion of their data, creating challenges for models trained on that data.

‍

Building a Scalable Data Foundation for AI Workloads

Object Storage Architecture for AI at Scale

Object storage provides the foundation for modern AI data infrastructure, offering several advantages over traditional file or block storage. The architecture scales horizontally by adding nodes to a cluster, enabling capacity and performance to grow together.

Modern object storage implementations optimize for AI workloads through features like high-throughput data paths, efficient small object handling, and integration with GPU-accelerated networking. Some systems implement direct GPU memory access, eliminating CPU bottlenecks and reducing data transfer latency.

Optimizing Data Pipeline Performance

Data pipelines transform raw data into formats suitable for model training, performing operations like cleaning, normalization, and augmentation. 

Pipeline optimization begins with understanding data access patterns and bottlenecks. Profiling tools identify where pipelines spend time—whether reading from storage, transforming data, or writing results. Once bottlenecks are identified, targeted optimizations address the limiting factors.

Managing Petabyte-Scale Datasets Efficiently

As datasets grow to petabyte scale, management challenges intensify. Organizations need systems to organize data, track versions, and manage the lifecycle of datasets from creation through archival or deletion.

‍

Ready to build your generative AI infrastructure? Request a free trial to experience high-performance object storage designed for AI workloads, delivering the sub-10ms latency and limitless scalability your generative AI initiatives demand.