Generative AI denotes an advancing classification of AI techniques capable of producing original content, including but not limited to, audio, visual content, and outputs consisting of text or computer code. A few noteworthy applications include ChatGPT, Midjourney, and GitHub Copilot. Familiarity with the foundations of generative AI should foster the appropriate and most efficient application of these emerging technologies.
It is one of the most rapid advancements shifting from a concept used by researchers to a common everyday application of AI. It is now employed daily by millions of people to draft everyday emails, create art and images, write requested computer code, and create text summaries of lengthy documents. Despite this rapid growth in application, the basic tenets of generative AI remain cloudy for most.
So, what does actually make AI "generative"? How does this sub-field of AI differ from the other classifications of AI? And what should the consumer know prior to application? The intent of this writing is to address the previously mentioned questions in the most straightforward manner, minimizing the use of hyperbole.
To begin, what is meant by generative AI?
It is a reference used in the field of AI to describe the systems and solutions that have the capability to produce original content as opposed to systems and solutions that analyze or classify content. An example of a common non-generative AI system is a system that determines the spam-non spam classification of an email. An example of this is a system that creates an email.
It can be learned by training on large data sets that could include text, images or even computer code. Languages can be learned and classified through layers of statistical and mathematical computations, which is at the core of this. Based on the data learned, the user of the system provides an input or a prompt, and the system produces the content that corresponds to the user's request.
- Text-based output includes articles, summaries, code, translations, and chatbot responses.
- Image output includes photorealistic images, illustrations, and concept art.
- Audio output includes voiceovers, music, and sound effects.
- Video output includes short video clips, animations, and other video output.
What is the underlying technology behind generative AI?
Large Language Models (LLM) and Transformers
Most text-based such tools (e.g. ChatGPT, Claude, and Gemini) use large language models. LLMs use a type of neural network architecture called a transformer. Google researchers introduced the transformer in their 2017 paper, "Attention Is All You Need."
Transformers process entire sequences of text simultaneously. This helps the model understand the overall context of long passages of text. Because of this, LLMs are able to generate text that is coherent, and answer complex instructions and questions.
Diffusion Models for Images
Such tools that create images (e.g. Midjourney and DALL·E) use a method called diffusion. Models that use this method learn how to reverse a noise-adding process. During the training process, a clean image is altered by adding random noise, and the model learns how to create the original image. During the inference process, the model starts from complete noise and refines the noise to produce a coherent image.
What does "training" refer to in the context of generative AI?
Training such AI MODEL requires the model to be exposed to a significant amount of training data. The internal parameters of the model are adjusted to an extreme degree until the model is able to generate outputs that appear to be realistic. For instance, GPT-4 was trained on text data that consists of hundreds of billions of words. The required infrastructure to perform this highly intensive task is significant, and therefore large AI research companies perform this type of work.
What generative AI tools lead the market today?
Differentiating by category, the market leading technologies include
- ChatGPT (OpenAI) – versatile text generation and coding support
- Claude (Anthropic) – document parsing and logic-intensive writing
- Gemini (Google) – document processing and multimodal tools
- Midjourney – advanced image generation
- GitHub Copilot – coding support and suggestions
- Sora (OpenAI) – text-to-video support (early access)
Depending on the use case, the optimal solution will vary. For text generation and research, ChatGPT and Claude provide useful starting points. image generation, use Midjourney, as it outputs the highest quality images.
What are the applications ?
Rapid Content Production
It has the ability to produce rapid content. Many use this to create rough drafts of content, advertising copy, and email sequences. Many blocks of text produced by this type of AI are treated as the start of a creative endeavor, as opposed to a final draft.
Augmented Software Development
OpenAI Codex, a descendant of the company’s flagship LLM, helps create, enhance, and debug software. In-house research by GitHub shows the users of this tool are capable of completing development tasks almost 55% faster. This is some of the most compelling evidence of productivity impact due to the introduction of this.
Automated Data Analysis
Long LLMs are able to read and process the data residing in a document by identifying the important units in a document, and generating a concise summary. Many legal teams use this to parse through a long list of contracts. Many researchers use this to summarize long research articles. Many enterprises provide their unstructured data to these tools to derive an answer.
Customer support
Many companies utilize LLM-based chatbots to manage basic customer support queries autonomously. These bots can be cost-effectively configured to answer support queries with the right company documentation.
What limitations and risks should you keep in mind?
It encompasses great capabilities, however understanding its limitations is essential to implementing them.
- Hallucinations: LLMs can be prone to describing nonexistent scenarios or answering questions incorrectly with a high degree of confidence. This occurs because models prioritize producing outputs that seem high quality, and are in no way optimizing for correctness. Be sure to validate any assertions, especially those involving specific data or incidents, AI tools generate.
- Training data cutoffs: The majority of contemporary LLMs are aware of a finite time span of historical data, and therefore do not understand newly developed data. Training data for ChatGPT-4o, for example, ends in early 2024.
- Bias: It's models emulate the biases contained in the training data. This occurs in insidious ways, such as imbalances in generated representations and stereotypical language. Knowing about the limitations of these tools allows for critical utilization of the models.
- Intellectual property ambiguity: The legal ramifications and frameworks of AI-generated outputs are still maturing. AI tools produce outputs in an indeterminate legal realm in most jurisdictions. Creating outputs for a profit will be safer if you track the progress made in this area.
How to begin using generative AI?
You do not need to be a tech wizard to grasp this methodology:
- Pick a point of interest. Focus on a task that you are accustomed to. Consider things like drafting an email, summarizing something, or conducting your first formal writing. Do this first.
- Understand how to formulate good prompts. Your output is reliant on your input. The more context you supply, the better your output. For example, be specific about your structure, your desired tone, and your audience.
- Outputs are drafts. Be sure to fact check AI generated content. Ensure that the content reflects your tone and adheres to your control standards.
- Changes/iterations are encouraged. Use the previous output as a barometer for what is needed or not. Be open to extensive modifications before settling.
- Stay updated. The pace that generative AI is progressing at is unprecedented. Your best bet to keep up would be to follow the leads at OpenAI, Anthropic, and Google DeepMind.
Where things are headed in generative AI
With the advent of OpenAI's GPT-4o and Google's Gemini Ultra, we are moving into a realm where processing and output of text, images, sounds and video are unified. In addition to this, the scope of what we can do is enhanced by the use of extremely large context windows. Some models can capture entire books or large inputs of code.
The focus of the future is on agentic AI. Agentic systems are capable of iteratively developing plans and executing tasks with minimal human intervention. Tasks may include coding, web browsing, and tool integration.
Start Simple
AI systems appreciate curiosity and trial and error. This document offers a basic understanding of the inner workings and limitations of Large Language Models (LLMs) and the tools that accompany them. With this knowledge, you should be able to explore independently.
The best way to learn is to try. Explore a tool and identify a task to accomplish using the tool. The best way to learn about the technology is to learn by doing.
Frequently Asked Questions
Is ChatGPT generative AI?
No, ChatGPT is a product of OpenAI and is classified as a specific example of a piece of generative AI technology. There exist other technologies in the same class as ChatGPT including Claude (Anthropic), Gemini (Google), Midjourney, and GitHub Copilot.
Will I be able to use such AI tools if I can't code?
Coding skills are not a requirement for the majority of AI tools. Programs such as ChatGPT and Midjourney are developed for ease of use to a general audience. Developers who use AI tools through APIs will need to know how to code, but that is not a requirement for the majority of applications.
How accurate is output that comes from Generative AI?
Output accuracy is dependent on the specific tool and the task or subject at hand. Some AI tools are known to “hallucinate” or have high levels of confidence in incorrect output. For fact based research, output from AI tools should be validated against an authoritative source prior to any further use.
Is content produced by such tools identifiable?
AI detection tools do exist, but are known to generate false positives and false negatives. Content that has gone through AI generation and has had human edits is particularly hard to detect.
What sectors are being impacted most by the advent of Generative AI?
Significant impacts of this are being most heavily felt in Marketing, Software Development, Legal Services, Healthcare, Education, and Media.