🔗 What actually happens when you press ‘send’ to ChatGPT

Disclaimer: The details in this post have been derived from the official documentation shared online by the OpenAI Engineering Team. All credit for the technical details goes to the OpenAI Engineering Team. The links to the original articles and sources are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.

ChatGPT is one of the most widely used applications built on top of large language models (LLMs).

Developed by OpenAI, it represents a major leap in how humans interact with artificial intelligence. Instead of using fixed commands or rigid search queries, conversations can happen naturally, in plain language, across a wide range of topics. This ease of interaction has made ChatGPT a familiar tool for millions of people, whether for learning, brainstorming, coding help, or everyday problem-solving.

Behind this simple interface lies a powerful set of technologies.

At the core is a transformer-based language model trained on vast amounts of text to predict the next word or token in a sequence. Over the past few years, these models have evolved from basic text generators into sophisticated systems that can reason, follow instructions, use tools, and personalize interactions. Advances in model size, training methods, serving infrastructure, and safety techniques have made it possible to deliver responses in real time to users around the world.

In this article, we will look closely at what actually happens when you press “Send” to ChatGPT. The journey begins with a secure request from the browser and continues through context preparation, tokenization, model inference, and streaming. We will explore how the system calls external tools when needed, applies multiple layers of safety, uses optional memory for personalization, and relies on performance optimizations to stay fast at scale.