How Does ChatGPT Work? What’s Under the Hood

ChatGPT today is built on the GPT-5 generation—a unified system that combines a “fast” general-purpose model, a “deep” model for demanding problems, and an intelligent router that switches between them depending on task complexity and your goal (e.g., when you explicitly ask for thorough reasoning). In practice, this means shorter response times for everyday questions and more rigorous answers where the model needs to think several steps ahead. (Official announcement: https://openai.com/index/introducing-gpt-5/ and system card: https://cdn.openai.com/gpt-5-system-card.pdf)

Core architecture: transformers, attention, and tokens

At its core are transformers—neural networks that, instead of reading text sequentially, use a “self-attention” mechanism. Thanks to it, the model can account for relationships between words across the entire context in a single step. This approach was first described in the research paper “Attention Is All You Need” and has become the standard in modern natural language processing. (Paper: https://arxiv.org/abs/1706.03762)

Internally, text isn’t processed as whole words but as tokens (small chunks of text). The model learns to predict the next token based on the previous ones. When generating an answer, it assembles the output token by token—the result is fluent text.

How the model learns: pretraining → fine-tuning → learning from human feedback

1. Pretraining: the model “reads” huge volumes of text and learns general language structures and facts.
2. Fine-tuning: adapting it for conversational use (following instructions, politeness, brevity vs. detail).
3. RLHF (Reinforcement Learning from Human Feedback): people compare answers and their preferences steer the model’s behavior.

With GPT-5, a new safety technique called safe-completion was added. Instead of hard refusals, it aims for answers that are as helpful as possible—while still safe in sensitive areas. (GPT-5 safety training overview: https://openai.com/index/gpt-5-safe-completions/)

How ChatGPT generates an answer in practice

The input text is split into tokens, and for each next token the model computes probabilities. Parameters such as temperature (degree of randomness) and top-p influence whether the output will be more creative or more conservative. Context also matters—the conversation history is part of the input, so ChatGPT can build on previous questions and clarifications.

Multimodality and “when to think more”

GPT-5 handles text as well as visual perception (e.g., describing images or working with diagrams) and can “understand” when a quick answer is enough and when it should think longer. The system router in GPT-5 decides whether to use the fast or deep mode—based on the task type, difficulty, and your explicit instruction (e.g., “think step by step”). (GPT-5 system card: https://cdn.openai.com/gpt-5-system-card.pdf)

Strengths and limitations in 2025

Strengths: summarization, rewriting in a different style, explaining concepts, generating outlines, working with code (including multi-step debugging and architecture suggestions), the ability to coordinate multiple tools.
Limitations: when context is insufficient, the model may “make up” details (hallucinations), it’s sensitive to how a prompt is phrased, it has a limited context length, and it cannot replace expert oversight in legal/medical decision-making.

How GPT-5 performs in coding (verified benchmarks)

According to official developer materials, GPT-5 sets a new high on key tests—for example 74.9% on SWE-bench Verified and 88% on Aider polyglot. In practice, this means faster project kickoffs, more reliable bug fixing, and better understanding of larger codebases. (Developer overview: https://openai.com/index/introducing-gpt-5-for-developers/)

Quick practical guide: how to write better prompts

• Be specific: what you want, for whom, and in what format (paragraphs, a table, a step-by-step list).
• Provide an example: a short sample of tone or style helps a lot.
• Break the task into steps: “first A, then B, finally C.”
• Set a role: “You’re an editor who will polish the style and preserve the author’s voice…”
• Add constraints: what to definitely omit, which sources to prefer.
• Iterate: prompt → answer → clarification → finalization.

For more advice on how to use AI better, read our article: ChatGPT in Slovak: the best tips, tricks, and practical prompts

When ChatGPT is a good fit—and when to use it with caution

Good fit: quick overviews, brainstorming, rewriting and stylistic editing, outline preparation, explaining complex topics, rapid analysis of texts and documents.
With caution: whenever you need up-to-date legal/medical guidance, high-stakes decisions, or work involving sensitive data—always verify and add human review. (General principles and limitations are also summarized in the official GPT-5 announcement: https://openai.com/index/introducing-gpt-5/)

Sample workflow

1. Task: “Summarize this 20-page report into 8 bullets for management.”
2. Refinement: “Use a neutral tone, include 3 risks and 3 opportunities, and end with a suggested next steps.”
3. Verification: ask for links to sources or citations, or a short table.
4. Finalization: request a version for a newsletter or a presentation.

Video: Introducing GPT-5 (official channel)

A short official video with an overview of what’s new:

Video: GPT-5 for work and productivity

Examples of how GPT-5 “thinks” appropriately for the topic and proposes next steps:

Sources

1. OpenAI – Introducing GPT-5: https://openai.com/index/introducing-gpt-5/
2. OpenAI – GPT-5 System Card: https://cdn.openai.com/gpt-5-system-card.pdf
3. OpenAI – Introducing GPT-5 for developers (SWE-bench, Aider benchmarks): https://openai.com/index/introducing-gpt-5-for-developers/
4. Vaswani et al. – Attention Is All You Need: https://arxiv.org/abs/1706.03762