What Is Generative Ai

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Mar 03, 2025

16 minutes read

What is generative AI?

Generative AI is making waves around the world, but how does it work — and how can businesses benefit from it?

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As excitement continues to build around the capabilities of generative AI and employees begin to embrace it in their daily work, tech leaders and engineers are exploring ways for GenAI to have the biggest business impact.

Promises of increased productivity and efficiency are an effective lure for business leaders—but before they’ll fully buy-in and invest, those same leaders need to better understand what generative AI is, how it can be incorporated into their workflows, and what the tangible impact might be.

Many business decision-makers are surprised by both the breadth of tasks GenAI  helps to solve, yet also the subtle ways in which AI tools can make mistakes.

Complexity grows fast when it comes to adjusting to different needs. There are myriad primary functions for generative AI, from conversational customer support to suggesting strategies for disaster response. Not all those who adopt a GenAI-driven strategy will use it in the same way.

So, what does this technology do? How does it work, and how can you use it to drive productivity in your business? And, most importantly,  what are its strengths, its challenges, and what is the future of generative AI?

##### What is generative AI and how does it work?

Simply put, generative AI is a form of artificial intelligence that creates new content based on user input. For example, if the user input is a question, the output will be an answer.

Generative AI refers to an artificial intelligence large language model (LLM) that can create original content by learning from patterns in existing content. Each generative AI model is trained to analyze enormous quantities of source material (such as text, images, and audio files.) and create an “answer”. This yields outputs that are similar in style and composition to the input, but essentially new.

The efficacy and reliability of GenAI can vary depending on the model and the tool. Enterprise-grade AI models use sophisticated algorithms and require vast sets of training data to deliver reliably, regardless of whether they’re designed to solve complex problems or automate laborious tasks.

The output of a GenAI model is normally steered by a process known as “prompting”. During this, the user explains—typically in “natural language,” which is the way humans speak every day—what they're hoping to generate.

###### What is a prompt in generative AI?

Essentially, a prompt is some form of instruction, directive, or request for a response from a GenAI model. Prompts can take many forms, including:

• Purely instructional input, such as telling the model what tone of voice to use
• Examples of prior responses to direct the model to produce a similar or dissimilar response
• Additional information the model should consider when generating a response
• General directives, such as telling the model to format output in a specific way.

Any prompt should match the capabilities and training of the generative AI model  in use While some AI models  have been trained more simply, and on more specific tasks, others have had a broader training and, consequently, a broader understanding. There’s little point asking a specialized AI to complete an action that it hasn’t been trained for—say, for example asking a customer support virtual assistant to analyze some code.

It's also possible to narrow down a direction and push back against errors or misinterpretations through successive prompting. This is a process where the user meets the output of each prompt with a further prompt, effectively teaching the model how to find the right answers.

##### Generative AI explained: introducing transformer-based models

Very important to generative AI is a “ transformer model”, which is a type of deep learning model first described in the 2017 paper co-authored by Cohere CEO and Co-Founder Aidan Gomez and titled “ Attention is All You Need”. These models can translate text and speech in near-real-time, making them fundamental in natural language processing (NLP) and so helping GenAI to communicate in a more natural way. “Natural” in this context means more like humans speak and write every day; some GenAI output is barely distinguishable from human-written content.

A transformer is based on a neural network architecture, and leverages a technique known as “self-attention”. Transformers differ from earlier AI technology as they do not process data in a linear, sequential manner. Instead, a transformer empowers the model to process different parts of a prompt at the same time. This simultaneous handling leads to better performance on long-range dependencies—that is, those times where elements in a sequence, like words in a sentence or data points in a time series, significantly influence each other even when they are far apart in the sequence.

###### How does a transformer-based model work?

Transformers process prompts in a very different way from other, older GenAI models.

• Tokenization and vectorization: The prompt is first converted into mathematical representations known as tokens, which splits the prompt into smaller units that are easier for the model to understand. The tokens then get converted into vectors known as embeddings. Embeddings represent the semantic meaning of each of the tokens. Lastly, positional encodings are added, and these are used by the model to understand the order of tokens used in this prompt.
• Encoder-decoder: Transformers can have encoder-only architecture, decoder-only architecture, or both. An encoder processes the input and generates contextualized representations. A decoder...