Generative AI refers to artificial intelligence systems that can create new content from text, images, music, code, and even video. Unlike traditional AI, which classifies or predicts based on existing data, generative AI produces original output based on the patterns it has learned.
How It Works
Generative AI is powered by models trained on massive datasets. These models learn to recognize patterns and relationships in language, images, or sound. When you ask a question or give a prompt, the model generates content by predicting what comes next — one token at a time.
Popular examples include:
- Text: ChatGPT, Claude, Gemini
- Images: DALL·E, Midjourney, Stable Diffusion
- Music & audio: Suno, MusicGen
- Code: GitHub Copilot
These tools are built on transformers, a type of neural network architecture that allows models to handle context, attention, and long-form content generation.
What Can Generative AI Do?
Generative AI can:
- Write emails, blog posts, and stories
- Translate or summarize content
- Generate images from text prompts
- Create realistic voice or video content
- Assist in programming and debugging
But it also raises concerns around:
- Environmental cost of model training and usage
- Bias in training data
- Misinformation (deepfakes, hallucinations)
- Privacy & intellectual property
Real Examples
- A marketing team uses Jasper AI to write ad copy in 5 languages.
- A student uses ChatGPT to rewrite their thesis summary more clearly.
- An artist uses DALL·E to generate illustrations based on text descriptions.
Generative AI is transforming how we create and communicate. While it offers powerful tools for language, design, and productivity, it also calls for responsible use, especially in multilingual and public contexts.
So, how do you talk to a generative AI model like ChatGPT to get the results you want?
The answer lies in how you design your prompts. In the next article, we’ll explore the basics of prompt engineering — from simple questions to advanced techniques.
👉 Read next: What is Prompt Engineering?
Curious about the energy and cost behind each article? Here’s a quick look at the AI resources used to generate this post.
🔍 Token Usage
Prompt + Completion: 3,000 tokens
Estimated Cost: $0.0060
Carbon Footprint: ~14g CO₂e (equivalent to charging a smartphone for 3 hours)
Post-editing: Reviewed and refined using Grammarly for clarity and accuracy
Tokens are pieces of text AI reads or writes. More tokens = more compute power = higher cost and environmental impact.