Skip to content

Snowflake Summit '25

Snowflake's annual user conference is returning to San Francisco. Register today and save on a full conference pass.

sign up now

Generative AI: Architecture, Models and Applications

Unlike traditional AI, which focuses on pattern recognition and predictions, generative AI learns from vast data sets and generates entirely new outputs.

  • Overview
  • Generative AI Architecture: The Five Layers
  • Types of Generative AI Models
  • Real-World Applications of Generative A
  • Resources

Overview

Generative AI (gen AI) is a rapidly evolving field of artificial intelligence that enables machines to generate content, including text, images, video and synthetic data. Unlike traditional AI, which focuses on pattern recognition and predictions, gen AI learns from vast data sets and generates new outputs. It powers applications ranging from personalized marketing and search enhancement to advanced product design and content creation.

To develop and deploy gen AI effectively, a robust architecture is required, enabling seamless data processing, model training, feedback integration, deployment and monitoring. Additionally, different types of AI models, such as large language models (LLMs), generative adversarial networks (GANs) and variational autoencoders (VAEs), play a crucial role in shaping gen AI's capabilities.

Generative AI Architecture: The Five Layers

A structured approach to gen AI development includes five key layers.

1. Data processing layer

  • The foundational layer of gen AI involves data collection, cleaning and transformation.

  • Data sources may include internal repositories, third-party data sets or user-generated inputs.

  • Feature engineering enables models to train on relevant information while filtering out noise.

2. Gen model layer

  • The AI model is trained and fine-tuned to generate high-quality outputs.

  • The model selection depends on the use case (for instance, LLMs for text or GANs for images).

  • Techniques such as transfer learning allow businesses to customize pre-trained models for specific tasks.

3. Feedback and continuous improvement layer

  • AI models improve over time by incorporating user feedback.

  • Methods include surveys, interaction tracking and reinforcement learning to enhance model accuracy.

  • Feedback loops detect bias, hallucinations or inaccurate responses, refining model outputs.

4. Deployment and integration layer 

  • AI models are integrated into applications via APIs, cloud platforms and enterprise software.

  • Infrastructure considerations include scalability, security and compute resources.

  • Seamless frontend and backend integration ensures businesses can leverage generative AI effectively.

5. Monitoring and maintenance layer

  • Once deployed, models must be monitored for accuracy, performance and ethical considerations.

  • Businesses must periodically update and retrain models based on new data and evolving requirements.

  • Resource allocation and cost optimization are crucial for maintaining AI efficiency.

Types of Generative AI Models

There are three main types of gen AI models.

1. Large language models (LLMs)

  • These are trained on vast text corpora, enabling text generation, summarization, translation and question-answering.
  • They are used in unstructured data insights, chatbots, content automation and intelligent search engines.

2. Generative adversarial networks (GANs)

  • A generator creates synthetic data, while a discriminator evaluates authenticity, refining outputs over time.
  • These are widely used in image/video synthesis, deepfake technology and artistic content generation.

 3. Variational autoencoders (VAEs)

  • These are encoder-decoder networks that learn latent representations of data, enabling anomaly detection, image generation and synthetic data set creation.

Real-World Applications of Generative AI

Gen AI can be used in various ways across business functions. Here are seven real-world applications of gen AI.

1. Data generation and augmentation:

Gen AI creates synthetic data sets when real-world data is limited, helping enterprises improve machine learning models.

2. Enhanced data search and discovery

AI-powered search engines understand natural language queries, offering relevant responses and data insights without requiring complex queries.

3. Personalized marketing and customer engagement

AI generates customized recommendations, emails, product descriptions and advertisements, increasing user engagement.

4. New product design and prototyping

AI-driven simulations help engineers, architects and designers test multiple product variations by helping create renditions before committing to physical production.

5. Creative content and media production

Marketing teams use gen AI to create social media posts, video scripts, logos and ad campaigns, optimizing creative workflows.

6. AI-powered business intelligence and analytics

AI assists in data visualization, forecasting and automated report generation, making business decision-making more efficient.

7. AI-powered business intelligence and analytics

Search engines enhanced with gen AI generate summaries, suggest related topics and refine user queries, improving information retrieval.