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What is Generative AI? - Explanation & Meaning

Learn what generative AI is, how it creates new content, and why GenAI is a game-changer for businesses in 2026. Discover LLMs, diffusion models, and more.

Definition

Generative AI (GenAI) is a category of artificial intelligence capable of creating new content — including text, images, code, audio, and video — based on patterns learned from training data.

Technical explanation

Generative AI employs different architectures depending on the type of content being generated. Large language models (LLMs) such as GPT-5 and Claude 4 are based on the transformer architecture and generate text by predicting the next token in a sequence. Diffusion models like Stable Diffusion and DALL-E 3 generate images by gradually removing noise from a random starting point, guided by a text prompt. Variational autoencoders (VAEs) and generative adversarial networks (GANs) are alternative architectures for image generation. In 2026, enterprise adoption of GenAI has grown explosively: over 72% of large enterprises have integrated GenAI into at least one business process. Multimodal models that can simultaneously process and generate text, images, and audio have become the standard. Retrieval-Augmented Generation (RAG) is widely deployed to reduce hallucinations and ground outputs in business-specific data. The rise of AI agents that combine GenAI with tool use marks the next phase in generative AI evolution.

How MG Software applies this

MG Software leverages generative AI to build powerful solutions for clients. We develop AI assistants that can summarize business documents, answer customer queries, and generate reports. Using RAG architectures, we ensure generated content is always grounded in up-to-date business data.

Practical examples

  • A law firm using GenAI to analyze contracts, extract relevant clauses, and generate first drafts of legal summaries, reducing document review time by 60% for attorneys.
  • A marketing agency deploying generative AI to create personalized campaign copy in multiple languages, reducing campaign production time from two weeks to two days.
  • A software company using AI-powered code assistants to generate boilerplate code, write unit tests, and accelerate code reviews, boosting developer productivity by 35%.

Related terms

artificial intelligencelarge language modelragprompt engineeringfine tuning

Further reading

What is artificial intelligence?More about large language modelsWhat is RAG?

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Frequently asked questions

Traditional AI systems are designed to classify, predict, or recognize patterns in existing data. Generative AI goes a step further: it creates entirely new content that did not literally exist in its training data. Where a traditional AI model classifies an email as spam or not-spam, a generative model can write a completely new email.
Yes, the main risks include hallucinations (generating plausible-sounding but factually incorrect information), bias (reproducing prejudices from training data), and intellectual property concerns. This is why it is essential to validate GenAI output, use RAG systems for factual grounding, and establish clear governance around AI usage.
Generative AI is evolving rapidly. In 2026, multimodal models are the norm, AI agents can autonomously execute complex tasks, and enterprise adoption is widespread. The focus is shifting from experimentation to production-ready implementations with emphasis on reliability, governance, and measurable ROI.

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