Generative AI understand: GPT, Midjourney & more

Generative AI understand: GPT, Midjourney & more

Short: Generative AI has conquered the technology world by storm.

Generative AI has conquered the technology world by storm. From ChatGPT to Midjourney to Sora – these systems can write texts, create images and even generate videos.

But how do these technologies actually work? This article explains the basics in a comprehensible way and shows the options of generic AI for companies.

What is Generative AI?

Generative AI designates systems of artificial intelligence that can create new content – unlike analytical AI that evaluates existing data.

These systems have been trained with huge amounts of data and can create completely new texts, images, music or code on this basis.

The difference at a glance

Generative AI understand: from GPT to Midjourney.

If Generative AI understand: GPT, Midjourney & more is on your roadmap, Cost Calculator: AI Development und Discover solutions outline services and next steps.

Analytical AI: "Is this email spam?" → Classifies existing data

Generative AI: " Write an email..." → Create new content

The most important types of generic AI

1. Large Language Models

Language models such as GPT-4, Claude or Gemini are expert in the processing and generation of text.

You understand context, can answer questions, write texts and even create code.

Examples: ChatGPT, Claude, Google Gemini, Llama

Applications: Text creation, translation, summaries, programming

2. Image generators (Diffusion Models)

Short: These systems create images from text descriptions.

These systems create images from text descriptions. They were trained with millions of picture text couples and can produce photorealistic or artistic images.

Examples: Midjourney, DALL-E 3, Stable Diffusion, Adobe Firefly

Applications: Marketing Visuals, Conceptual Art, Product Design

3. Video generators

The latest generation of generic AI can create videos from text descriptions or images. This technology is rapidly developing.

Examples: Sora (OpenAI), Runway, Pika

Applications: Advertising clips, Explaining videos, Social Media Content

4. Audio and music generators

AI systems can compose music, synthesize voices and create sound effects.

Examples: Suno, ElevenLabs, Mubert

Applications: Podcasts, background music, voice over

How do Large Language Models work?

LLMs like GPT are based on the Transformer architecture and have been trained with enormous amounts of text from the internet.

Simplifiedly, you have learned what word statistically is most likely to come next.

Training in three steps

• Pre-Training: The model learns from billions of texts the structure and patterns of language

• Fine tuning: The model is adapted to specific tasks and desired behavior

• RLHF: Reinforcement Learning from Human Feedback – People rate answers to improve quality

Comparison of leading models

Model Suppliers Strengths Special features

GPT-4 OpenAI versatility, reasoning Multimodal (text + image)

Claude 3 Anthropic Long contexts, security Up to 200k Token Context

Gemini Ultra Googl

References and further reading

The following separate references complement the topics in this article:

• Bitkom – German digital industry association.
• German Federal Office for Information Security (BSI).
• European Commission – Digital strategy.
• MDN Web Docs (Mozilla)
• W3C – World Wide Web Consortium.

"DevOps is less about tools and more about shared ownership of quality and release discipline."

— Björn Groenewold, Managing Director, Groenewold IT Solutions

Frequently Asked Questions (FAQ)

What is this article about: “Generative AI understand: GPT, Midjourney & more”?

Here we cover Generative AI understand. GPT, Midjourney &. More — focused on architecture, process, and business outcomes. In short: Generative AI understand: from GPT to Midjourney.

Basic knowledge about large language models, image generators and their applications.

Who benefits most from the content described here?

Typical readers are business and IT leaders in AI training who want to secure quality, security, and ease of upkeep over the long term.

How does this topic fit into an IT or digital strategy?

In a digital strategy, prioritize stable core processes first, then extensions. See also professional software development and consulting.

For multi-system landscapes, IT consulting and architecture helps align vendors and internal teams.

What are sensible next steps if we need support?

If you need support with design, delivery, or modernization: schedule an appointment or outline your project via contact.