How does Generative AI work?

Generative AI uses neural networks like transformers and diffusion models, which detect patterns in large amounts of data. This helps in creating new content from a prompt provided by a user.

What are the different types of Generative AI?

The different types of Generative AI include Transformer-based models: Generative Pre-trained Transformers (GPT) Diffusion models: Stable Diffusion, DALL-E, Midjourney Generative Adversarial Networks (GANs) Other types of Generative AI include Variational Autoencoders (VAEs), Autoregressive Models, and Flow-based Models.

Is ChatGPT the same as Generative AI?

ChatGPT and Generative AI are different. Generative AI is the broader category of AI that has the capability of creating new content. ChatGPT is only a specific chatbot developed by OpenAI that uses generative AI models like GPT-4.

What is the difference between generative AI and traditional AI?

Traditional AI mainly does analysis, classification, or prediction of outcomes from existing information. On the other hand, generative AI creates brand-new content. For example, a traditional AI system can classify an image, but a generative AI system can create a brand-new image from a text prompt.

What are the applications of generative AI?

The main uses of generative AI include content creation for marketing, software development, and customer support. Other uses of generative AI include faster drug development, 3D product development, and generating synthetic data to train other models in healthcare, finance, and entertainment.

What are the potential risks of generative AI?

The possible risks of generative AI are data privacy violations, algorithm bias, and security risks. Generative AI can also hallucinate content and cause copyright infringement. So you should always double-check content generated by Generative AI.

What are the ethical considerations of Generative AI?

The ethical considerations of Generative AI are called Responsible AI. Responsible AI means reducing bias, being transparent, and avoiding the creation of DeepFakes. Some of the key considerations of Responsible AI are protecting IP rights, the environment, and human accountability.

What is RAG in Generative AI?

Retrieval-Augmented Generation is an approach where an LLM is connected to an external data source. The data source is a vector database where factual information is stored. When a query is made to the Generative AI model, it retrieves real-time data from the vector database to give context to the LLM.

What is the difference between LLMs and Generative AI?

The difference between LLMs and Generative AI is that Generative AI is an umbrella term for any type of AI that creates content. LLM stands for Large Language Models. LLM is a subset of Generative AI but is only used for text generation, whereas Generative AI does text, image, video, and other forms of media generation too.

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Last updated Oct 27, 2026

Start learning: stage 1 Look up a term

What is generative AI?

Generative AI is a category of artificial intelligence that creates new content, including text, images, code, audio and video, by learning statistical patterns from large datasets. It is powered by architectures like transformer models, large language models, diffusion models and generative adversarial networks, and businesses use it to automate content workflows, build AI agents and process documents at scale.

According to McKinsey's The state of AI in 2025, 88 percent of survey respondents report regular AI use in at least one business function, up from 78 percent a year earlier.

Survey respondents reporting regular AI use in at least one business function

A year earlier: 78%
2025: 88%

Source: McKinsey's The state of AI in 2025

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1The idea

Generative AI

2The engine

3Shaping it

4Putting it to work

5At scale

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The learning path

The map shows how the ideas fit together. To learn them, read these six stages in order: each one builds on the one before.

Stage 1 of 6 · 4 terms
Written by Paresh Mayani
Foundational Concepts
Start here if you're new to AI. These articles cover the building blocks that everything else is built on.
Up next: stage 2 · Prompt Engineering and Interaction
Stage 2 of 6 · 4 terms
Written by Lokesh Dudhat and Paresh Mayani
Prompt Engineering and Interaction
Learn how to talk to AI models effectively in different ways, from basic prompting to advanced techniques.
Up next: stage 3 · Agentic AI and Automation
Stage 3 of 6 · 5 terms
Written by Lokesh Dudhat and Paresh Mayani
Agentic AI and Automation
See how AI systems are growing from reactive chatbots to autonomous, goal-driven agents.
Up next: stage 4 · Core Techniques and Infrastructure
Stage 4 of 6 · 3 terms
Written by Lokesh Dudhat and Paresh Mayani
Core Techniques and Infrastructure
Check out which methods and infrastructure make generative AI work in real-time.
Up next: stage 5 · Generative Models and Architectures
Stage 5 of 6 · 3 terms
Written by Lokesh Dudhat and Paresh Mayani
Generative Models and Architectures
Dive deep into the model architectures that do the content generation.
Up next: stage 6 · Data and Training
Stage 6 of 6 · 3 terms
Written by Paresh Mayani and Lokesh Dudhat
Data and Training
Understand how AI models are fed, trained, and improved with data.

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Showing 24 of 24 terms

Agentic AI and Automation · Stage 3

Agentic AI

AI that plans and acts toward an objective over multiple steps, rather than answering one prompt at a time.

Updated Aug 7, 2026 · By Paresh Mayani

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Frequently asked questionsabout generative AI

Short answers to the questions people ask before they plan a generative AI build.

Jay Nandwana Technical Business Consultant at SolGuruz

Jay NandwanaSenior Technical Business Consultant

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Generative AI uses neural networks like transformers and diffusion models, which detect patterns in large amounts of data. This helps in creating new content from a prompt provided by a user.
The different types of Generative AI include
- Transformer-based models: Generative Pre-trained Transformers (GPT)
- Diffusion models: Stable Diffusion, DALL-E, Midjourney
- Generative Adversarial Networks (GANs)
Other types of Generative AI include Variational Autoencoders (VAEs), Autoregressive Models, and Flow-based Models.

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Generative AI Hub

What is generative AI?

How the ideas connect

The learning path

Foundational Concepts

Prompt Engineering and Interaction

Agentic AI and Automation

Core Techniques and Infrastructure

Generative Models and Architectures

Data and Training

Now look up any term in the dictionary

The dictionary

Agentic AI

AI Agents

AI Hallucinations

AI Orchestration

Context Engineering

Conversational AI

Data Augmentation

Diffusion Models

Fine Tuning LLMs

Foundation Models

Generative Adversarial Networks

Generative AI

Large Language Model

Model Context Protocol

Multi-Agent Systems

Multimodal AI

Neural Networks

Prompt Engineering

Retrieval Augmented Generation

Synthetic Data

Transformer Architecture

Vector Database

What Is RLHF?

Zero-Shot, One-Shot and Few-Shot Prompting

Frequently asked questionsabout generative AI

Planning an AI build?Talk to Paresh and team.

By Working With SolGuruz,You Will Get a Combo of Technology + Management.