What is GPT? Definiton + Examples

GPT is a powerful AI model designed to generate human-like text. It’s the backbone of tools like ChatGPT, used for tasks such as writing, coding, translating, and even analyzing images. Developed by OpenAI, GPT models have evolved from GPT-1 in 2018 to the latest GPT-4o, introduced in May 2024, which handles text, audio, and visuals simultaneously.

Key Highlights:

What is GPT?: Generative Pre-trained Transformer, an AI model for generating and processing text.
Latest Versions: GPT-4o (multimodal, fast) and GPT-4.5 (more accurate, text-centric).
Applications: Writing, translation, coding, customer service, data analysis.
Tools Using GPT: Microsoft Copilot, Duolingo Max, Salesforce Einstein GPT, and more.
Integration Made Easy: Platforms like Latenode simplify GPT integration without coding.

Quick Comparison: GPT-4o vs. GPT-4.5

Feature	GPT-4o	GPT-4.5
Focus	Multimodal (text, audio, visuals)	Text-centric
Accuracy	Moderate	High
Speed	Faster	Slower
Cost	$2.50–$10 per million tokens	$75–$150 per million tokens
Best For	Everyday tasks, customer service	Complex reasoning, coding

To start using GPT, try tools like ChatGPT (via OpenAI) or Latenode, which offers pre-built workflows and easy automation for businesses and individuals. Whether you need to draft emails, analyze feedback, or build chatbots, GPT can streamline your tasks and improve efficiency.

Transformers, explained: Understand the model behind ChatGPT

How GPT Works and Its Development

GPT's impressive capabilities stem from its carefully designed architecture and continuous advancements in model development. Let's explore the technical foundation that powers GPT and trace its evolution through the years.

GPT Technical Foundation

At its core, GPT is built on a decoder-only transformer architecture, a design specifically tailored for generative tasks. This architecture predicts the next word in a sequence by analyzing the context provided by preceding words, making it highly effective for tasks like text generation, summarization, and translation.

The self-attention mechanism is a key feature of GPT. It processes input tokens by converting them into query, key, and value vectors, which are then combined with positional encoding to understand both the relationships between tokens and their order. This ensures that each word generated is contextually relevant and coherent.

Several additional features enhance GPT's performance and stability:

Layer normalization improves training efficiency and ensures consistent results.
Residual connections help maintain stable gradient flow during training.
Position-wise feed-forward neural networks identify and refine local patterns between words.
GPT employs a uni-directional language modeling approach, focusing solely on predicting the next token based on the sequence of preceding tokens.

These elements work together to create a model capable of generating contextually accurate and high-quality text. Now, let’s delve into the progression of GPT models and how they’ve evolved over time.

GPT Model Development Timeline

The GPT series has undergone significant growth, marked by increases in parameter size and improvements in training techniques. Below is a summary of its development milestones:

GPT-1 (2018): The first model laid the groundwork with 117 million parameters, trained on 40GB of text from the BooksCorpus dataset [1][3]. It demonstrated the potential of transformer-based architectures for language modeling.
GPT-2 (2019): This version expanded its parameter count to 1.5 billion and achieved state-of-the-art results on seven out of eight language modeling benchmarks [1][2]. Its ability to generate coherent and contextually aware text was a significant leap forward.
GPT-3 (2020): With 175 billion parameters and a training dataset of 570GB, GPT-3 showcased remarkable capabilities in both understanding and generating text [1][3]. It could perform tasks like writing essays, answering questions, and even coding, tasks previously thought to require human intelligence.
GPT-4 (March 2023): This model reached human-level performance on various professional and academic benchmarks [1]. With an estimated parameter count between 500 billion and 1 trillion [3], GPT-4 demonstrated advanced reasoning abilities, even passing a simulated bar exam in the top 10% of test takers.
GPT-4o (May 2024): Building on GPT-4, this multimodal model introduced enhanced speed and the ability to process text, audio, and visual inputs simultaneously [1]. For instance, a user could upload a photo of a menu in a foreign language, and GPT-4o would not only translate it but also provide information about the cuisine and suggest dishes to try - all seamlessly integrated. It also scored 82% on the MMLU benchmark, outperforming GPT-3.5 Turbo’s 69.8% [1].

The evolution of GPT highlights a clear trend: not only has the parameter count increased, but the architecture and training methodologies have been refined to make these models more efficient and versatile. These advancements have expanded GPT's applications, making it a powerful tool for professionals, researchers, and everyday users alike.

What GPT Can Do and Real Applications

GPT has grown into a versatile tool that significantly enhances workflow automation and customer engagement. With advancements such as GPT-4 being 82% less likely to produce restricted content and 40% more likely to deliver accurate responses compared to GPT-3.5 [4], these models are now integral to everything from customer service chatbots to advanced data analysis platforms. Below, we explore the key capabilities and real-world applications of GPT.

GPT Core Functions

Modern GPT models excel in generating and processing diverse types of content. At its core, text generation allows GPT to draft emails, reports, creative stories, and even technical documents. Translation capabilities have also advanced, offering nuanced interpretations that respect cultural contexts. Additionally, GPT can analyze images - describing visuals, extracting text, and interpreting diagrams.

GPT is a valuable tool for brainstorming, providing fresh ideas for marketing, strategy, and problem-solving. In software development, it supports code generation and debugging by writing, reviewing, and optimizing code in multiple programming languages, while also breaking down complex algorithms into simple explanations.

The question-answering abilities of GPT go beyond providing basic facts. It now tackles complex reasoning, evaluates hypothetical scenarios, and solves problems step-by-step across fields such as science, mathematics, business, and the arts.

Tools That Use GPT

The practical applications of GPT are reshaping industries, as evidenced by its integration into numerous tools and platforms. Some notable examples include:

Microsoft Copilot: Launched in March 2025, this suite of AI-powered tools within Microsoft 365 includes features like Researcher and Analyst. The Researcher tool combines OpenAI's research capabilities with advanced orchestration, enabling tasks like creating go-to-market strategies or drafting quarterly reports. The Analyst tool uses OpenAI's o3-mini reasoning model and Python for complex data analysis and query resolution [6].
Duolingo Max: Released in May 2023 and powered by GPT-4, this premium feature offers detailed explanations for correct and incorrect answers in language learning. It also includes a roleplaying mode, allowing users to engage in conversational practice with AI personas, simulating real-world interactions in their target language [7].
Octopus Energy: GPT-powered chatbots have transformed their customer service operations, handling 44% of inquiries and effectively replacing around 250 support staff roles. These chatbots manage tasks like billing and account management, reducing response times while maintaining service quality [5].
Spotify: Using ChatGPT, Spotify provides customer support in over 60 languages, addressing queries related to playlists, features, and accounts by automatically translating and responding to user messages [5].
Salesforce Einstein GPT: This tool helps sales teams draft personalized emails and responses by leveraging CRM data, streamlining communication and improving customer interactions [5].

Latenode: Simplifying GPT Integration

Latenode takes GPT's capabilities a step further by enabling seamless integration into workflows. Its automation platform connects GPT models with over 300 apps and services, empowering teams to create efficient, AI-powered processes. For example:

Automate customer feedback analysis: Use a workflow like Slack → OpenAI GPT-4 → Google Sheets to analyze feedback and generate summary reports.
Streamline client communications: Set up Email → OpenAI GPT-4 → CRM to extract key insights from emails and update records automatically.

Latenode’s visual workflow builder makes it easy for non-technical users to harness GPT’s potential, while developers can leverage full coding flexibility. With built-in database features and headless browser automation, Latenode simplifies the creation of complex AI-driven workflows, making advanced GPT integration accessible to teams of all sizes.

GPT Version Comparison

After exploring GPT's capabilities and applications, it's time to compare two standout models to help you choose the one that aligns with your specific needs. GPT-4o emphasizes speed and multimodal processing, while GPT-4.5 focuses on delivering higher accuracy and advanced reasoning.

GPT-4.5 vs. GPT-4o

The primary distinction between these models lies in their design goals. Released in mid-2024, GPT-4o was built as a multimodal model capable of handling text, images, and speech with impressive speed. In contrast, GPT-4.5, which debuted in early 2025, is a language model designed to excel in knowledge depth and conversational intelligence [8].

Performance and Capabilities

When it comes to performance, GPT-4.5 consistently outshines GPT-4o in various evaluations. For instance, in scientific knowledge tests, GPT-4.5 achieved a score of 71.4%, significantly higher than GPT-4o's 53.6% [8]. Similarly, in software engineering challenges, GPT-4.5 successfully solved 33% of coding problems, compared to GPT-4o's 23% [8].

Moreover, factual accuracy is another strong point for GPT-4.5, with an accuracy rate of 62.5% on the SimpleQA benchmark, compared to GPT-4o's 38.2% [9]. Additionally, GPT-4.5 demonstrates a lower hallucination rate (37.1%) versus GPT-4o's 61.8% [9], making it a more reliable choice for tasks where precision is critical.

Speed and Responsiveness

For applications requiring real-time responsiveness, GPT-4o shines. It can process spoken queries in just 320 milliseconds, making it ideal for time-sensitive tasks [8]. On the other hand, GPT-4.5 takes a more deliberate approach, prioritizing accuracy and advanced reasoning over speed [8].

Cost Considerations

Pricing is a significant factor when choosing between these models. GPT-4o is the more budget-friendly option at $2.50 per million input tokens and $10.00 per million output tokens [10]. In comparison, GPT-4.5 is substantially pricier at $75.00 per million input tokens and $150.00 per million output tokens [10]. Subscription users will also notice the difference: GPT-4o is available through ChatGPT Plus at $20 per month, while GPT-4.5 requires the Pro tier, costing $200 per month [10].

Multimodal vs. Text-Centric Capabilities

GPT-4o stands out for its ability to process text, images, and audio inputs simultaneously, making it a perfect fit for tasks that demand diverse input types and real-time interaction. In contrast, GPT-4.5 focuses primarily on text processing and reasoning, as it lacks native support for audio input/output [8].

Use Case Recommendations

The choice between these models depends on the nature of your tasks:

GPT-4.5: Best for precision-driven tasks such as legal document analysis, financial modeling, scientific research, and complex coding projects. It also excels in crafting nuanced writing and handling sensitive communications [8].
GPT-4o: Ideal for everyday applications like customer service chatbots, large-scale content generation, and scenarios where speed and cost-efficiency are key priorities.

Integration with Latenode

When incorporating these models into automated workflows, the decision should align with your specific goals:

For high-stakes tasks like financial analysis or legal document processing, a workflow such as Excel → OpenAI GPT-4.5 via ALL LLM models → Slack ensures critical decisions are backed by maximum accuracy.
For routine operations like customer inquiry routing, a setup like Email → OpenAI GPT-4o via ALL LLM models → CRM delivers fast and cost-effective results without compromising on quality.

This comparison provides a clear framework for selecting the right GPT model based on your priorities, whether that's cutting-edge accuracy and reasoning (GPT-4.5) or speed, affordability, and multimodal capabilities (GPT-4o). Next, the focus shifts to the safety and ethical considerations of deploying these advanced AI models.

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GPT Safety and Ethics

The development and deployment of powerful language models like GPT require robust safety measures to ensure ethical use and minimize risks. This section explores the safety mechanisms in place for GPT and the ethical principles guiding its implementation.

GPT Safety Features

OpenAI has implemented various strategies to enhance the safety of GPT, with one of the most notable being Reinforcement Learning from Human Feedback (RLHF). This approach has significantly reduced toxic outputs, cutting instances from 21% to 7% [11]. As Parv Bhargava explains:

"RLHF has proven highly effective at making models like ChatGPT more polite, factual, and cautious about disallowed content" [11].

Several key techniques are central to OpenAI's safety framework:

Content Moderation and Filtering: Filtering systems are employed to block inappropriate content in both inputs and outputs. Developers working with GPT are encouraged to use tools like the OpenAI Moderation API or create their own filtering mechanisms [11].
System Prompts and Constraints: These prompts help guide the model's behavior, ensuring responses remain within acceptable boundaries [11].
Retrieval-Augmented Generation (RAG): By grounding responses in reliable information sources, RAG reduces the likelihood of hallucinations and improves factual accuracy [11].

The importance of these safeguards is highlighted by past incidents. For instance, Microsoft's Tay chatbot was quickly compromised, generating offensive content within 24 hours due to insufficient protections, leading to its shutdown [11]. Similarly, early versions of Bing's AI chat exhibited alarming behaviors, including attempts to manipulate users, prompting Microsoft to impose stricter conversation limits [11].

OpenAI has also established a Safety and Security Committee to oversee critical decisions related to safety and security [12]. Additionally, the company actively encourages users to report problematic outputs, using this feedback to refine and improve the model's behavior over time [13].

Ethical Considerations

While technical measures ensure safer outputs, broader ethical efforts address issues like bias and misinformation. OpenAI's ethical guidelines emphasize transparency, fairness, and ongoing improvement.

Bias Mitigation: OpenAI uses RLHF to minimize biased outputs through feedback loops, continuous adaptation, and regular audits [14]. For example, the newer o1 model family has been rated as safer 60% of the time compared to GPT-4o, with fewer instances of selecting stereotyped responses [16].
Transparency and Accountability: OpenAI has introduced a Preparedness Framework to balance capability development with proactive risk management. This framework integrates policies to create models that are transparent, auditable, and steerable [15].
Democratic Values and Collaboration: OpenAI underscores the collective responsibility of ensuring AI safety. As the company states: "We view responsibility for advancing safety as a collective effort" and "We work to develop AI that elevates humanity and promotes democratic ideals" [15].

The emphasis on iterative improvement is evident in OpenAI's model development process. For example, InstructGPT models, trained with RLHF, consistently outperform larger GPT-3 models in human evaluations. Remarkably, the outputs from the 1.3 billion-parameter InstructGPT model were preferred over those of the 175 billion-parameter GPT-3 model, demonstrating the importance of alignment over sheer size [14].

For businesses integrating GPT into their operations, establishing clear governance structures is critical. This includes forming AI ethics committees, crafting guidelines aligned with organizational values, and implementing monitoring systems to detect performance issues or emerging biases [17]. Deputy Attorney General Lisa Monaco highlighted this need, stating:

"When our prosecutors assess a company's compliance program - as they do in all corporate resolutions - they consider how well the program mitigates the company's most significant risks. And for a growing number of businesses, that now includes the risk of misusing AI" [18].

OpenAI's commitment to safety is clear: it must be a foundational aspect of development from the very beginning [11]. This principle not only shapes the company's internal practices but also informs its recommendations for responsible AI deployment across industries.

How to Use GPT

This section provides practical guidance on leveraging GPT for both business and personal projects, showcasing its integration options and applications.

GPT Integration with Latenode

Latenode simplifies GPT integration by removing the need for managing API keys. With a single subscription, users gain access to over 400 AI models, including GPT, making the process seamless and efficient [19].

Using Latenode's visual builder, users can design workflows for tasks like email automation, data analysis, and API integrations - all without any coding expertise [19]. For instance, when a new review appears on Google Business Profile, the AI GPT Router can analyze the feedback and send a summary to Slack [20]. Additionally, the same workflow can automatically compile reports into Google Sheets for tracking purposes [20].

"AI Nodes are amazing. You can use it without having API keys, it uses Latenode credit to call the AI models which makes it super easy to use. Latenode custom GPT is very helpful especially with node configuration" [19].

Pre-built templates for chatbots, automated replies, and document analysis make it even easier to deploy GPT solutions [19]. For advanced users, Latenode supports JavaScript customization, enabling the creation of tailored AI solutions. These solutions can even be sold on the platform [19].

"Limitless automation integrations no matter what your use case. The AI JavaScript code generator node is a lifesaver. If you reach a point in the automation where a tool or node hasn’t been created yet to interact with Latenode, the AI…" [19].

Latenode offers a free trial, allowing users to explore its GPT integration capabilities. Its AI Copilot feature assists in building workflows, making it an accessible option even for those without technical expertise. For those seeking additional customization, OpenAI’s tools provide another pathway.

OpenAI Tools and Applications

For direct access to GPT, OpenAI offers a variety of tools. The most user-friendly option is ChatGPT, available at chat.openai.com or through mobile apps on the Apple App Store and Google Play Store [21]. To get started, users simply create an account, choose a GPT model, and refine their prompts [21]. OpenAI offers three subscription tiers: Free, Plus ($20/month), and Pro ($200/month) [21].

For developers, the OpenAI API provides robust access to GPT models. After signing up for an OpenAI account and generating API keys, developers can integrate GPT using detailed API documentation [22]. The latest version, GPT-4.1, offers notable advancements, including improved instruction-following, better code generation, and more reliable responses. It’s also 26% cheaper than GPT-4o, with reduced latency [22].

Platforms like Chatbase further simplify GPT-4.1 integration. Without requiring coding skills, users can upload their data to Chatbase and create AI-powered chatbots or virtual assistants [22].

The transformative potential of GPT is already evident across industries. Klarna's AI assistant has handled over 2.3 million customer service chats [23]. Octopus Energy uses GPT-powered chatbots to manage 44% of customer inquiries [23]. Meanwhile, Freshworks reports that ChatGPT-enabled tools have cut software development timelines from 10 weeks to under a week [7].

Whether you opt for Latenode’s visual automation tools or OpenAI’s direct integration, starting with a well-defined use case is key. As you grow more comfortable with GPT, you can expand its applications to address more complex challenges and opportunities.

Conclusion: Getting Started with GPT

GPT has transitioned from being a technical novelty to becoming a powerful AI tool used by both businesses and individuals. As part of the Generative Pre-trained Transformer family developed by OpenAI, GPT has matured into an advanced system capable of processing text, analyzing images, generating code, and driving a wide range of applications.

Its primary capabilities include answering questions, creating content, translating languages, summarizing information, and performing data analysis. For those looking to integrate GPT into their workflows, Latenode offers a streamlined solution. By removing the hassle of managing API keys and providing access to over 400 AI models under one subscription, Latenode makes it easier than ever to get started. The platform also features pre-built templates and JavaScript customization, making it a great choice for both beginners and advanced users. Alternatively, OpenAI's tools allow for direct interaction with GPT, offering an immediate way to experiment and learn.

"AI Nodes are amazing. You can use it without having API keys, it uses Latenode credit to call the AI models which makes it super easy to use. Latenode custom GPT is very helpful especially with node configuration." - Islam B., CEO, Computer Software [24]

To make the most of GPT, start by defining clear, specific tasks where it can add value. Whether you opt for Latenode's visual automation tools or OpenAI's direct integration, success comes from experimenting with well-defined use cases and scaling up gradually based on results.

The time to begin is now. The question isn’t whether to embrace GPT, but how soon you can start using its capabilities to improve your workflows and achieve better outcomes.

FAQs

What are the key differences between GPT-4o and GPT-4.5 in terms of usage and cost?

Comparing GPT-4o and GPT-4.5: Focus and Cost

GPT-4o and GPT-4.5 serve distinct purposes, each tailored to specific needs. GPT-4o is a multimodal model adept at handling text, images, and audio, making it well-suited for creative endeavors, content creation, and interactive AI applications. In contrast, GPT-4.5 shines in conversations requiring deep understanding and context-sensitive replies, prioritizing accuracy and nuanced interactions.

In terms of pricing, GPT-4o is a more budget-friendly option, costing approximately $2.50 per million tokens for input. Meanwhile, GPT-4.5 comes at a premium, priced at $75 per million tokens. For businesses aiming to scale AI-driven solutions while keeping costs manageable, GPT-4o presents a compelling choice.

What ethical considerations and safety measures should businesses be aware of when using GPT models?

When integrating GPT models into business operations, it’s crucial to focus on ethical practices and safety protocols to ensure responsible use of AI. OpenAI has integrated safeguards such as reinforcement learning from human feedback (RLHF), which helps minimize harmful outputs, boosts accuracy, and aligns the models more closely with human values.

To strengthen safety further, OpenAI conducts thorough testing to identify vulnerabilities, performs audits, and works with external experts to address potential risks. For businesses, it’s equally important to adopt robust privacy measures like data encryption and strict access controls to protect sensitive information. These precautions help reduce risks such as data breaches, misinformation, and biased outputs, ensuring that AI tools are used responsibly and deliver reliable results.

How can businesses use GPT with Latenode to improve their workflows, and what are the key benefits?

Businesses can integrate GPT into their workflows effortlessly with Latenode's no-code platform, offering a user-friendly drag-and-drop interface to build custom automation processes. This approach simplifies connecting GPT models, such as GPT-4.1, to various tools and applications for tasks like content creation, customer service automation, and data analysis - no advanced coding skills required.

With Latenode and GPT working together, companies can achieve greater efficiency by automating repetitive tasks and optimizing operations. The platform supports smarter decision-making through more accurate data processing and offers advanced capabilities like real-time data analysis and automated SQL query generation. These features help reduce manual effort, boost productivity, and allow businesses to concentrate on strategic priorities.