ChatGPT

ChatGPT is OpenAI’s conversational AI assistant, built on the company’s GPT family of large language models (LLMs), neural networks trained to predict and generate human-like text. It answers questions, writes and debugs code, analyzes data, drafts documents, and increasingly acts as an agent that plans multi-step tasks and uses tools to finish them. Released to the public in November 2022, ChatGPT introduced most people to generative AI and has since become the default entry point for professionals experimenting with AI at work. For developers, it matters because the same GPT models power OpenAI’s API, so patterns you learn inside ChatGPT, like prompting and tool use, transfer directly to production systems you build.

How ChatGPT works

ChatGPT is a transformer-based language model, a neural network architecture that predicts the next word in a sequence by weighing the relevance of every other word around it. OpenAI trains the underlying GPT model on massive amounts of text, then fine-tunes it with reinforcement learning from human feedback (RLHF), a technique where human reviewers rank sample responses so the model learns to prefer helpful, accurate answers over merely plausible ones. Here is how the pieces fit together:

Model tiers: ChatGPT runs on different GPT-5.5 model sizes depending on your plan, trading off speed, cost, and reasoning depth within the same underlying architecture.
Context window: the model reads your entire conversation, including uploaded files, as tokens, small chunks of text, within a fixed window, letting it track long conversations without losing earlier details.
Tool use: ChatGPT can search the web, run code, generate images, and call custom tools that developers define through the API, deciding when a tool is needed and how to use the result.
Agent Mode: for multi-part tasks, ChatGPT can plan a sequence of steps, use tools across multiple turns, check its own output, and keep working until the task is done, rather than stopping after one reply.
Memory: ChatGPT can retain facts and preferences across sessions, so you do not need to repeat instructions in every new conversation.

What you can build with ChatGPT

Customer support assistant: wire the API into a support widget that answers tickets using your product docs, escalating to a human when confidence is low.
Content and research pipeline: automate drafting blog posts, summarizing long reports, or extracting key data points from PDFs at scale using the Batch API.
Internal knowledge tool: connect ChatGPT to your company’s docs and wikis so employees can ask questions in plain language instead of searching manually.
Coding assistant integration: use the API inside a custom developer tool to review pull requests, generate tests, or explain unfamiliar code.
Data analysis agent: feed spreadsheets or CSVs and have ChatGPT identify trends, build visualizations, and write a plain-English summary for non-technical stakeholders.