How ChatGPT works and what Large Language Models are

In short: ChatGPT and other Large Language Models (LLMs) work by predicting the next word in a sequence, one token at a time, using the Transformer architecture introduced by Google in 2017. They do not «reason» like a human: they compute probability distributions across billions of parameters trained on massive text corpora. All the leading 2026 models — GPT-5, Claude 4.7, Gemini 2.5 — share this mathematical foundation.

• Transformer & self-attention: the Vaswani et al. paper «Attention Is All You Need» (2017) has over 200,000 citations on Google Scholar and is the foundation of every modern LLM
• Training scale: 2026 frontier models range from hundreds of billions up to trillions of parameters, trained on trillions of tokens (Stanford CRFM — AI Index 2025)
• Context window: Gemini 2.5 Pro reaches 1M tokens of input, Claude 4.7 Sonnet handles 200K-1M tokens, GPT-5 handles 400K (official Anthropic, Google DeepMind, OpenAI docs)

This in-depth article was written by Francesco Galvani, CEO of Deep Marketing, branding strategy instructor, science communicator, and developer of neural networks and artificial intelligence systems for marketing since 2003. The goal: to explain in an accessible way how Large Language Models work in 2026, with verifiable data from official AI lab documentation.

What is a Large Language Model?

A Large Language Model (LLM) is a neural network trained to predict the next word — more precisely the next «token» — given an input text sequence. The word «large» refers to two dimensions: the number of parameters (the internal connections of the model, in the order of hundreds of billions) and the volume of training data (trillions of tokens, collected from the public web, books, code, and scientific articles).

When you ask ChatGPT «what is the capital of Italy?», the model does not «search» for the answer in a database. It computes, token after token, which word has the highest probability of following the previous sequence given the distribution learned during training. The string «Rome» emerges because, in the training data, it was statistically the most frequent completion of that context. It is statistical prediction, not retrieval.

The shared definition of LLM adds three characteristics: emergent capabilities (abilities that appear only above certain scale thresholds, such as solving multi-step problems), generality (the same model handles translation, summarization, code, and question answering) and prompt sensitivity (the output changes radically depending on how you phrase the question).

How does an LLM learn?

Training a 2026 frontier LLM is typically articulated in three phases, as described in technical reports from OpenAI, Anthropic and Google DeepMind.

1. Pre-training. The model reads trillions of tokens of public text (Common Crawl, Wikipedia, books, GitHub code, arXiv papers) and learns to predict the next token. It is the most expensive phase: the training clusters of 2025-2026 frontier models use tens of thousands of GPUs for weeks or months, with estimated costs exceeding 100 million dollars per single run (Stanford CRFM — AI Index Report 2025).

2. Supervised fine-tuning (SFT). The base model is refined on (prompt, ideal-answer) pairs written by human annotators. Here the model learns to respond as a helpful assistant, not just to continue text.

3. Reinforcement Learning from Human Feedback (RLHF). Humans rank the model's responses and a second model (reward model) learns what is «good». The LLM is then optimized to maximize the reward. It is the phase that distinguishes ChatGPT from the base GPT: it makes the model more aligned, polite, and refusing on dangerous requests. Anthropic uses a variant called Constitutional AI (Bai et al., arXiv 2022).

Transformer architecture in 3 minutes

The Transformer architecture, presented in the paper «Attention Is All You Need» (Vaswani et al., 2017), replaced recurrent networks (RNN/LSTM) as the de-facto standard for language. Three key concepts:

Tokenization and embedding. The text is split into tokens (sub-words) and each token is mapped into a high-dimensional numerical vector (embedding) that encodes meaning in mathematical form.

Self-attention. The heart of the Transformer. For each token, the model computes how much to «attend» to every other token in the sequence. In the sentence «the bank was closed because it was a holiday», the attention mechanism connects «closed» to «bank» (financial institution, not a riverbank) using the context «holiday». It is the ability to look at the entire context simultaneously — not sequentially as in RNNs — that makes Transformers superior.

Layer stack & feed-forward. Dozens of attention blocks are stacked. At each layer, the representation of each token becomes more abstract and contextualized. After 60-120 layers, the vector of the last token contains enough information to predict the probability distribution of the next token.

The autoregressive model generates the response one token at a time: it predicts token 1, adds it to the sequence, predicts token 2, and so on. Each «new» token sees everything that came before — including the user's prompt and the tokens already generated.

GPT-5 vs Claude 4.7 vs Gemini 2.x in 2026

In 2026, the four frontier labs — OpenAI, Anthropic, Google DeepMind, Meta — offer models with different public specs. The table below summarizes data from official documentation: maximum context window, knowledge cutoff, stated strength. Note: benchmarks (MMLU, HumanEval, GPQA) change month by month and must be verified on public leaderboards such as Stanford HELM or LMArena.

The most useful distinction for a 2026 marketer is not «which is the best» (it changes every 3 months) but «which is right for the task». Claude 4.7 Sonnet tends to win in structured long-form writing; GPT-5 excels in mathematical reasoning; Gemini 2.5 Pro is unbeatable when you need to process an 800-page PDF or an entire codebase in a single prompt; Llama 4 is the only serious option if self-hosting and data control are required.

Limits and hallucinations

LLMs produce fluent responses even when they are factually wrong: it is the phenomenon known as «hallucination». The cause is structural: the model optimizes the probability of the next token, not truth. When there was enough signal in the training data about the topic, the model gets it right; when the topic is rare or requires recent post-cutoff facts, the model «makes things up» convincingly.

The paper «A Survey on Hallucination in Large Language Models» (Huang et al., arXiv 2023, updated 2025) classifies hallucinations into factuality hallucination (wrong data) and faithfulness hallucination (answer not faithful to the prompt). Mitigation relies on three tools: retrieval-augmented generation (RAG) — the model cites an external database; tool use — the model calls a calculator or search engine instead of guessing; chain-of-thought with verification — the model reasons step-by-step and a second model checks.

Other structural limits: knowledge frozen at cutoff (GPT-5 knows nothing about events after September 2024 without tools), absence of persistent memory across conversations (without dedicated features such as ChatGPT Memory), bias inherited from training data, vulnerability to prompt injection. According to Stanford HAI — AI Index Report 2025, hallucination rates of frontier models on factuality benchmarks dropped 40% between 2023 and 2025 but remain far from zero.

Applications in marketing

LLMs do not replace marketing strategy but multiply operational productivity. The most established applications in 2026:

• Structured content production: SEO briefs, first drafts of long-form articles, multi-language translation with tone of voice preservation. The human role shifts from writer to editor-strategist.
• Classification and analysis: structured extraction from customer emails, reviews, sales call transcripts. Repetitive tasks that used to require hours of manual work become batches of minutes.
• Personalization at scale: email variants, landing page headlines, Meta Ads creatives generated based on segment and intent. Always to be tested with serious A/B tests, not with vanity metrics.
• Conversational search & GEO: optimizing content to be cited by ChatGPT, Perplexity, Google AI Overview (which means structure, FAQs, authoritative sources cited).
• Workflow agents: automations with tool use (search, scraping, CRM updates) that 18 months ago required custom development and today run with frameworks such as LangChain or Anthropic Claude Agent SDK.

McKinsey — State of AI 2024-2025 finds that companies with mature GenAI adoption report higher time savings on content and marketing operations compared to other functions, but only a minority still have rigorous measurement of the impact on revenue.

Need help with AI and SEO in 2026?

Deep Marketing supports international brands in adopting LLMs for marketing, content, and conversational search optimization. Request a free audit or discover our SEO & GEO consulting designed to get you cited by ChatGPT, Claude, Perplexity, and Google AI Overview.

Frequently Asked Questions

What is a Large Language Model?

A Large Language Model is a neural network trained to predict the next token in a textual sequence, with hundreds of billions of parameters and trillions of training tokens. It is «large» by scale (model and data) and «language» because it operates on natural text. GPT-5, Claude 4.7, Gemini 2.5, and Llama 4 are all LLMs based on the Transformer architecture introduced in 2017.

How does ChatGPT answer questions?

ChatGPT receives the user's prompt, converts it into tokens, and generates the response one token at a time by predicting the most likely one given the context. Each new token is concatenated to the sequence and the model repeats the computation. It does not consult a database nor «think»: it runs statistical predictions over a distribution learned from billions of examples during pre-training and refined with RLHF.

What is the difference between GPT-5 and Claude 4.7?

They are both Transformer LLMs but differ in lab (OpenAI vs Anthropic), training data, alignment strategy (standard RLHF vs Constitutional AI), and public strengths: GPT-5 is tuned for mathematical reasoning and agentic coding with 400K context, Claude 4.7 Sonnet excels in long-form writing and handles 200K tokens up to 1M in beta. Knowledge cutoff and benchmark performance vary.

Why do LLMs hallucinate?

LLMs hallucinate because they are optimized to predict plausible tokens, not to tell the truth. When the topic is under-represented in the training data, or requires post-cutoff facts, the model completes the sequence with grammatically correct but factually invented text. Standard mitigations are RAG (retrieval from external databases), tool use (calls to search engines or calculators), and chain-of-thought with external verification.

Are LLMs really intelligent?

It depends on the definition of intelligence. LLMs show impressive emergent capabilities — multi-step reasoning, creative writing, coding — but operate by statistical prediction, not understanding. They have no consciousness, intentionality, or biological memory. The scientific community (Stanford HAI, MIT CSAIL) distinguishes between narrow intelligence (specific tasks, where LLMs excel) and human-like general intelligence, which remains an open goal.

Sources and References

• Vaswani et al. — Attention Is All You Need, arXiv (2017)
• Anthropic — Claude models documentation
• OpenAI — Platform models documentation
• Google DeepMind — Gemini API models
• Meta AI — Llama 4 Multimodal Intelligence
• Stanford HAI — AI Index Report 2025
• Stanford CRFM — HELM Leaderboard
• OpenAI — GPT-4 Technical Report, arXiv (2023)
• Bai et al. — Constitutional AI, arXiv (2022)
• Huang et al. — A Survey on Hallucination in LLMs, arXiv (2023)
• MIT Technology Review — Artificial Intelligence coverage
• McKinsey — The State of AI 2024-2025