Generative AI capabilities and limitations

Generative AI is a powerful part of modern artificial intelligence. Unlike tools that only analyze existing data, generative models can create completely new content, for example:

• Large Language Models (LLMs) like ChatGPT and Claude can write text that sounds human
• Tools like MidJourney and DALL·E can generate realistic images
• Apps such as DeepBrain can produce video content
• Speech generators can mimic human voices with surprising accuracy

These systems work by recognizing patterns in huge amounts of data and mixing them in new ways. This makes them useful for creative tasks like designing visuals, writing copy, or developing prototypes.

But generative AI also has clear limits. It doesn’t really understand the content it creates. Without tools like Retrieval-Augmented Generation (RAG), it can make things up, producing answers that sound right but aren’t true. It can also lose consistency in longer texts or repeat patterns instead of generating truly original ideas.

Image generators, for example, often struggle with small but telling details. A common giveaway is how they draw hands. AI might add too many fingers or twist them in unnatural ways. Spotting these errors helps people recognize when an image was made by a machine.^[1]