ChatGPT vs. Generative AI: Definitions and Distinctions

Artificial intelligence (AI) is reshaping the way industries operate, allowing for enhanced creativity and efficiency in almost every industry, including customer support, predictive analytics, risk management, and marketing.

Released in late 2022 as an advanced chatbot, ChatGPT garnered significant attention, and has since been developed into a multimodal large language model (LLM). This means that users can interface with ChatGPT using natural language, and now receive outputs as text, images, audio, and video.

The term generative artificial intelligence, on the other hand, refers to the broader field of study and innovation in which ChatGPT is a single, standout product. Generative AI encompasses foundational techniques and concepts, and ChatGPT puts these principles into practice in a specific way.

Keep reading to learn more about generative AI and ChatGPT, and how these technologies shape digital interactions.

Is ChatGPT a type of generative AI?

Yes, ChatGPT is a generative AI tool.

It's a specialized AI technology that creates human-like text responses, as well as relatively high-quality images, audio, and video. ChatGPT is part of a broader category of generative AI systems designed to be easy to use through a natural-language interface, and to produce new content. Developed by OpenAI, ChatGPT uses a large language model to analyze and process user prompts and generate relevant outputs.

What is generative AI?

Generative AI refers to a subset of artificial intelligence focused on creating new content, including text, images, audio, video, 3D models, and synthetic data. This field primarily uses machine learning algorithms, especially deep learning models, to discern patterns in training data and generate new outputs based on those patterns.

While some generative AI models use natural language processing (NLP) to analyze and reproduce language, others might focus on visual or auditory data without an NLP component. Through deep learning and, when relevant, NLP, these models can interpret user inputs and generate content that aligns with the AI’s training.

Examples of generative AI are Midjourney and DALL-E 3 for image generation, GitHub Copilot for code generation, and ChatGPT and Google Gemini, Google’s flagship generative AI model, which are both multimodal foundation models that are widely used for text generation. As the field evolves, new models and applications continue to emerge, expanding the boundaries of what AI can create.

What is ChatGPT?

OpenAI’s ChatGPT is an example of generative AI in action. Initially, ChatGPT was built on OpenAI’s GPT-3.5 to provide conversational experiences for users. Individuals submitted text inputs—also known as prompts—and ChatGPT analyzed the user inputs using NLP technology to generate outputs.

Earlier versions of ChatGPT offered general-purpose conversational abilities. With GPT-4 and now GPT-4o (the latest model available), the chatbot supports more complex tasks and multimodal inputs and outputs.

Due to its advanced features and functionality, ChatGPT attracts over 400 million users per week and supports numerous use cases. These include real-time chatbots and virtual assistants, music and lyric generation, and more. It’s also been a valuable tool for drafting emails, crafting resumes and cover letters tailored to specific job descriptions, and assisting in other writing tasks.

Generative AI vs. ChatGPT

From foundational concepts to adaptability and specialization, we explain how generative AI and the generative model ChatGPT interrelate.

Foundational concepts

At the heart of generative AI lies deep learning, a subset of machine learning that employs multilayered neural networks to capture intricate patterns in vast amounts of data. The networks are designed to link these patterns using complex statistical algorithms that form interrelated data structures. Once properly trained, these deep neural networks can be prompted to create high-quality outputs, making them foundational to generative AI.

Text generation, a prominent application of generative AI, heavily relies on these deep learning principles. LLMs, which are designed for text generation, use deep neural networks, particularly transformer architectures, to analyze and generate human-like text based on the patterns they’ve learned from extensive training data.

What makes the transformer architecture special is that the network is trained to look at all parts of a string at once. From this broad view of the data, it then determines which parts are most relevant and which are most closely related, in order to deliver usable outputs. This is a break from previous architectures, which looked at data sequentially.

ChatGPT, a multimodal LLM, is built on these deep learning foundations. By leveraging the power of deep neural networks and the principles of natural language processing, ChatGPT can effectively analyze user prompts in natural language and craft coherent and relevant responses.

Development and training

The development of generative AI models, including the LLM that ChatGPT is based on, is a complex process that begins with the design of neural network architectures tailored to specific tasks. These architectures are then implemented using machine learning frameworks like TensorFlow, Keras, and PyTorch (Python and Torch).

How these models are trained largely determines the kinds and quality of output they generate. The richness and diversity of the data sets they are trained on is pivotal to the model’s reliability. 

Contrary to popular belief, this data isn’t stored in the traditional sense that a computer stores data. Rather, the data is condensed in a similar manner that ZIP files are condensed. When users later interact with the LLM, the LLM doesn’t retrieve information from its training data. Instead, it runs the user’s input through the statistical models it built during its training.

After its initial training, a model will be fine-tuned to perform a specific kind of task.  Depending on the ultimate goal, fine tuning might include supervised learning, reinforcement learning from human feedback, and fine tuning for safety and quality assurance, among others.

For instance, the generative AI model that ChatGPT was originally built from—GPT3.5—was specialized to work with natural language via text. Its original data sets included books available in the public domain and a vast amount of data from the internet including Wikipedia, web pages scraped from Common Crawl, code repositories including GitHub, and academic papers, among other sources.

As OpenAI continued to develop their foundation model, they also trained it on images, videos, and music, among other multimodal sources, enabling users to work in these different media.

In contrast, Google’s Gemini was developed from the ground up as a multimodal AI. Its original training included more types of data, in theory making it less specialized for text than ChatGPT.

The actual output users get from any model, however, will depend on the quality of the model’s training, the sophistication of its algorithms, and the ability of the user to make clear, concise inputs.  

Mechanisms and architectures

Generative AI in general, including ChatGPT, is based on deep learning models, enabling it to both analyze and generate complex data structures. Generative AI often relies on architectures like generative adversarial networks (GANs).

GANs use a unique dual neural network system: the generator, which crafts new content, and the discriminator, which evaluates this content against real data to guide the generator’s improvements. In essence, the generator creates an output that the discriminator compares against a real sample. When the discriminator fails to tell the difference between the AI generated output and the real sample, the task is considered complete.

In contrast to this approach, ChatGPT’s foundation model is built from a generative pre-trained transformer (GPT) architecture. Instead of using adversarial learning, GPTs use an attention mechanism to weigh the importance of different words in a sentence, or different pixels in an image. 

This allows ChatGPT to maintain context, structure sentences effectively, and even refer back to previous parts of a conversation to generate contextually relevant responses. This design enables ChatGPT to analyze user inputs and, one character at a time, create relevant outputs.ChatGPT is one kind of the larger field of generative AI, which all rely on neural networks. 

Tools and products

Generative AI has sparked a wide range of tools tailored to specific tasks—from text and code to images and audio. Here's how ChatGPT compares to other top players in the space:

• ChatGPT (OpenAI). With GPT-4o as its latest model, ChatGPT supports text, image, and audio inputs. It powers everything from real-time chatbots and personal assistants to content generation and data analysis.
• Claude (Anthropic). Claude 3 is a rising competitor in the generative text space. Known for its longer memory and safer outputs, it's built for enterprise use and high-stakes applications.
• Google Gemini. Originally launching as Bard in 2023, Gemini is Google’s suite of generative models integrated into its Workspace tools, offering advanced search, summarization, and productivity features.
• GitHub Copilot. Built on OpenAI’s GPT models, Copilot supports developers by suggesting code snippets and completing tasks in real time. It’s integrated into IDEs like Visual Studio Code.
• Midjourney and DALL-E 3. Both are leading tools for AI image generation. Midjourney creates stylized, high-quality visuals, while DALL-E 3 (integrated with ChatGPT) allows for more controlled prompt-based image creation, including inpainting and prompt editing.
• Mistral and Mixtral (open-source). These newer models offer competitive performance in open-source generative AI. They’re gaining traction among companies seeking customizable AI without the limitations of proprietary models.
• Stable Diffusion. An open-source image generation model known for its flexibility and strong community support. It's widely used for visual experimentation and customization.

Together, these tools show how generative AI is evolving across modalities—text, code, images, and beyond—each with its own strengths.

Adaptability and specialization

Generative AI stands out for its adaptability and capacity to cater to different applications. It can help produce images, audio, and video, as well as craft text and 3D models.

Beyond content creation, generative AI plays a pivotal role in data augmentation. By generating new data sets that mirror the characteristics of the original training data, generative AI aids in refining and enhancing the performance of other machine learning models, ensuring they deliver more accurate predictions and classifications.

ChatGPT, while a product of generative AI, hones its capabilities on language tasks. Its design and training are optimized for analyzing and processing human-like text. This allows it to summarize research, draft emails, or assist in content creation for social media.

However, while ChatGPT is specialized for text, you may want to use other specialized generative AI tools, such as Midjourney or DALL-E 3, for visual content generation.

And remember that, because of how AI is trained, it can suffer “hallucinations” (output that meets the model’s statistical criteria, but that doesn’t match reality). Any output produced by generative AI should be reviewed for accuracy and tone.

Strategic considerations for businesses adopting generative AI

Knowing what generative AI can do is one thing—deciding how and when to adopt it is another. Strategic planning helps businesses maximize value while minimizing risks. Here are a few key considerations:

• Assessing ROI. Before jumping in, weigh the potential return against your investment. Look at time saved, changes in quality, or increased output. Generative AI doesn’t just replace manual effort—it can open up new revenue opportunities through faster content production or better customer experiences. But it isn’t the answer to every problem.
• Choosing the right tools. Not every use case requires the most powerful model. General-purpose tools like ChatGPT offer flexibility, while niche or fine-tuned models can provide higher accuracy for specific industries or tasks. The right choice depends on your goals and how you plan to use the technology. For example, businesses already using Microsoft 365 may benefit from generative AI integrations like Copilot, powered by OpenAI models.
• Scalability planning. A successful pilot doesn’t guarantee long-term success. Think about how the AI solution will integrate into your existing tech stack, what infrastructure changes may be needed, and whether your team has the skills to support ongoing use.

Strategic adoption starts with asking the right questions and then finding the right talent to help answer them. Through Upwork, you can reach professionals who specialize in evaluating, implementing, and scaling generative AI solutions.

Open-source vs. proprietary AI models

As generative AI continues to develop, one strategic choice businesses face is whether to adopt proprietary tools like GPT-4o or build on open-source models like Mistral or LLaMA 2.

• Proprietary models often offer cutting-edge features, better performance, and integrated services (e.g., ChatGPT with DALL·E and browsing). However, they may come with usage limits, higher costs, and less customization.
• Open-source models offer greater control, transparency, and flexibility. Companies can fine-tune them for specific use cases and host them on private infrastructure, making them a strong choice for AI applications requiring data privacy or unique functionality.

The best choice depends on your goals, technical resources, and use case complexity. On Upwork, you’ll find independent professionals who specialize in both, helping you evaluate and implement the right approach for you.

Ethical and societal implications

Generative AI has already reshaped many sectors. However, with these advancements come ethical and societal concerns that warrant attention.

From the broader landscape of generative AI to the specific realm of text generation tools like ChatGPT, these concerns are multifaceted:

• Inaccurate information. AI systems are only as reliable as their training data. If trained on erroneous or misleading data, they risk promoting inaccuracies, which is especially concerning for text-generation tools that might be used for information dissemination.
• Bias. If generative AI tools are trained on biased data sets, they can perpetuate and amplify these biases in their outputs. This is particularly problematic for text generators that might inadvertently produce biased or prejudiced content.
• Privacy concerns. The vast data sets that power generative AI can contain sensitive information. There’s a risk of these tools inadvertently leaking personal data or infringing on privacy.
• Potential misuse. Generative AI tools can be exploited to produce harmful content, from hate speech to misinformation. The rise of deepfakes and fake news underscores this concern.
• Copyright violations. Generative AI might inadvertently produce content that mirrors existing copyrighted works. This poses challenges in terms of intellectual property rights and compensation for original creators.

Practical applications

Modern AI applications are often a combination of different technologies working to achieve specific outcomes. By leveraging the strengths of various generative AI tools such as ChatGPT, we can create more comprehensive and effective AI solutions for business and beyond.

Let’s explore a few examples:

• Visual content creation. Imagine a project where AI-generated visual content requires detailed textual descriptions. Here, DALL-E could be tasked with generating the visual based on a brief, while ChatGPT provides a rich, detailed description. For instance, ChatGPT might generate a prompt like, “Design a steampunk-inspired cityscape at sunset,” and DALL-E would then produce the corresponding visual.
• Educational tools. The potential for AI in education is vast. An AI tutor could use a tool like Deep Dream to create graphical content illustrating a concept while using ChatGPT to guide a student through the lesson with explanatory text. This combination offers a multimedia learning experience catering to both visual and textual learners.
• Content generation and optimization. For content creators, ChatGPT can assist in drafting articles or marketing copy. Once the draft is ready, generative AI tools like Grammarly can refine the grammar and style, while SEO tools like Moz can suggest optimizations. ChatGPT can then integrate these suggestions, producing content that’s both engaging and SEO-friendly.
• Market research. In the realm of market analytics, tools like Semrush can provide detailed insights and reports. For stakeholders who might not be well-versed in technical jargon, ChatGPT can step in, translating these insights into easily digestible summaries or presentations.

How to build a generative AI strategy for your business

To see the hype yourself and get the most out of generative AI tools like ChatGPT, businesses need more than access—they need a clear strategy. Here’s a simple framework to help guide adoption and implementation:

• Goal setting. Start by identifying what you want generative AI to achieve—whether that’s improving efficiency, generating content faster, or supporting innovation. Clear goals shape the rest of your decisions.
• Talent sourcing. AI tools are only as good as the people behind them. Look for independent professionals with skills in areas like prompt engineering, data analysis, AI ethics, and system integration.
• Testing and iteration. Build quickly, test often. Start with minimum viable products (MVPs) or pilot projects. Use feedback loops to refine outputs and improve outcomes over time.

Hiring the right experts early on can help your team avoid costly missteps and unlock value faster. Platforms like Upwork give you access to AI specialists who can support every stage of the strategy.

The future of generative AI and ChatGPT

As we stand on the cusp of a new era in artificial intelligence, the horizon is filled with possibilities. Let’s explore what the future might hold for generative AI and ChatGPT:

• Natural language understanding. The advancements in natural language processing will refine AI’s ability to effectively process English and other languages. This inclusivity can democratize access, allowing AI narratives to be shaped by diverse cultures and perspectives.
• Multimodal features. Imagine a world where text generation intertwines with voice, visuals, and even tactile feedback. This blending of AI-generated content can offer immersive experiences where a story isn’t just read but is seen, heard, and felt.
• Real-time learning. The machine learning algorithms of tomorrow will be dynamic, adapting in real time to user interactions. This fluidity can make AI tools more responsive and intuitive.
• Human-AI synergy. Rather than replacing people in the workplace, AI can augment our capabilities. The workplaces of the future could be hubs of human-AI collaboration, where creativity is amplified and productivity increases.
• Personalization. AI systems will increasingly be able to tailor their outputs to individual preferences, curating experiences that resonate on a personal level.

As we contemplate these exciting prospects, the fusion of generative AI and ChatGPT promises a future in which technology doesn’t just serve us—it complements and elevates us.

Leverage generative AI with Upwork

ChatGPT is a form of generative AI that helps with content creation and information retrieval. In other words, generative AI is a broad field of artificial intelligence, while ChatGPT is a specific implementation of it.

Working with experts can help you unlock the potential of generative AI tools. Upwork can connect you to generative AI professionals or ChatGPT specialists who can help you implement artificial intelligence in your business or startup.

And if you’re an AI expert looking for work, start your job search on Upwork. With artificial intelligence jobs posted regularly, you can find projects that match your skills and earn extra income. Get started today!‍

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Disclosure: Upwork is an OpenAI partner, giving OpenAI customers and other businesses direct access to trusted expert independent professionals experienced in working with OpenAI technologies.

Upwork does not control, operate, or sponsor the other tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.