How does generative AI work? A simple explanation

Generative AI works primarily like a statistical model which, based on the context of the conversation, predicts the next fragments of a response. Instead of “searching” for a ready-made answer in a database, it builds it step by step, selecting the next tokens with the highest probability. This approach supports fluency and allows it to write in different styles, but it does not mean automatic fact-checking. That is why, in practice, it is worth providing specific input data, asking for explicit assumptions and checking critical elements such as numbers, dates and proper nouns. In the following sections I explain exactly what the model “does” during generation and why tokens matter for the length, cost and quality of the response.

how generative AI works: basic principles

Generative AI creates responses by predicting “what should come next” in the text based on what is already in the context. When you ask for a recipe or request an email, the model does not reach for ready-made text from a single database, but selects the next tokens according to a probability distribution learned from large data sets. This means that instead of rigid rules such as “if A, then B”, it works on the basis of probability, which handles language generation well but can be unreliable in tasks that require strict accuracy control. If you want to reduce the risk of mistakes, ask for sources, figures and assumptions, and verify the result step by step.

Generative AI has no intentions or experiences — it matches language patterns to the content of the conversation, and a confident tone is not proof of factual accuracy. Its language “flexibility” comes from training on a mix of data such as books, websites, code and articles, often also licensed data and content created by humans in annotation processes. In practice, this means the model can write in many styles and languages, but it can also carry over errors and biases present in the data. If you are wondering whether AI “reads the internet live”, usually it does not — unless the application is connected to a search engine or tools.

1. 01Context and patternsAnalysis of language patterns
2. 02Probabilistic predictionSelecting the next tokens
3. 03Token generationBuilding a response step by step
4. 04No intentA confident tone is just a pattern
5. 05Verification is keyCheck facts and sources

A probabilistic model generates language well, but requires verification in precise tasks.

the role of tokens and why they are key

Tokens are key because the model does not operate directly on words, but on tokens (that is, fragments of words), and it “counts” both input and output in tokens. For example, a single Polish word may be split into several tokens, which affects the rate at which the context limit and generation budget are used up. In practice, conversation length limits and costs are often accounted for in tokens; as a rough guide, 1,000 tokens usually amount to around 700–800 words in Polish, although this depends on the content. When a response suddenly cuts off, a common cause is a context limit or a limit on generated tokens.

Tokens are closely linked to how large a portion of the conversation the model “covers” at once in the so-called context window, which has a defined capacity (e.g. 4k, 16k or 128k tokens — depending on the model). If, in a longer exchange, the model stops remembering details from many screens back, this is usually because older parts have fallen outside the context window. To reduce this problem, it helps to summarise the current agreements, restate the key facts, or support yourself with memory tools such as notes, a knowledge base or RAG. For this reason, practical token management (the length and structure of the input) often determines the quality of the response.

understanding the mechanism of probability in AI

The probability mechanism in generative AI works in such a way that the model selects the next tokens based on a probability distribution rather than strict logic rules. In practice, this translates into the ability to create coherent, fluent responses because the model “recognises” language patterns learned from large text corpora. At the same time, in tasks that require strict fact control, it can be erratic, because it does not have a built-in truth verification mechanism. When a statement sounds very convincing, this is often primarily the effect of the style of the generated language, rather than a guarantee of correctness.

The same probabilistic approach explains why responses can differ between runs even with a similar prompt. Generation includes an element of controlled randomness, because the model considers not only one “most likely” next token, but also alternatives. If you want to reduce the risk of error, ask for sources, figures and assumptions, and verify the result step by step. It also helps to make questions more specific, because with unclear context the model may “guess” the intent and provide the wrong background instead of asking a clarifying question.

1. 01Probability distributionToken selection is statistical, not logical.
2. 02Language fluencyRecognises patterns from large datasets.
3. 03No truth verificationConvincing style, shaky facts.
4. 04Controlled randomnessResponses may differ.

Key point: AI’s probabilistic approach ensures fluency, but does not guarantee factual accuracy.

what is the transformer architecture and how does it influence AI

The Transformer architecture remains the dominant approach in modern generative text models because it scales well on GPUs and effectively learns dependencies in long sequences. Unlike older solutions, a Transformer can take many parts of the context into account at the same time, instead of analysing text line by line. A key role is played by the attention mechanism, which assigns weights to different parts of the context and helps the model “focus” on the important fragments during generation. As a result, when, for example, a specific date appears in the text, the model can assign greater weight to that element and treat less important sentences as background.

The Transformer consists of many layers that gradually transform the text representation into increasingly abstract forms: shallower layers better capture syntax and local dependencies, while deeper ones strengthen meaning and long-range connections. At the input, text is converted into vectors (embeddings), i.e. numbers describing semantic similarities based on training data, while positional encoding provides information about the order of tokens. During answer generation the model calculates the result iteratively, token by token, so longer outputs and larger models usually involve greater latency. In practice, this is helped by speeding things up with key and value caching (KV cache), because the model does not have to recalculate the entire context from scratch each time.

The impact of the Transformer is also visible on the cost side: training large models requires enormous computing power (often hundreds or thousands of GPUs) and hundreds of billions of data tokens. This is one of the main reasons why training a ChatGPT-class model “at home” is difficult due to the costs of hardware, energy and infrastructure. At the same time, smaller models can be trained or fine-tuned on fewer GPUs, although their quality and knowledge scope may be more limited. These properties of the architecture translate directly into how quickly, how long and how stably models generate text in real applications.

how models learn and adapt: pretraining and fine-tuning

Generative AI models absorb the basics of language during pretraining and then adapt to specific tasks through fine-tuning. Pretraining involves learning to predict the next token on huge corpora of text and code, which allows the model to statistically “learn” grammar, style, facts and reasoning patterns, without manually written rules. This is usually self-supervised learning, because the “label” is simply the next token. In practice, this stage enables the model to write fluently and understand many forms of expression.

Fine-tuning is used to adapt the model to a specific use case, for example customer support, legal style or generating product descriptions. When a company chatbot “speaks differently” from a public one, this is often the result of fine-tuning on company data and instructions. This uses, among other things, SFT (supervised fine-tuning) and LoRA/QLoRA techniques, which reduce training costs by updating only part of the parameters. In addition, RLHF is used in practice, where people rate responses and the model learns to prefer the more helpful and safer ones, which affects the conversational style and the tendency to refuse in risky situations.

1. 01Pretraining: absorbing the basicsLearning grammar, facts and patterns from huge corpora.
2. 02Self-supervised learningPredicting the next token in text and code, without rules.
3. 03Fine-tuning: task specialisationAdapting to specific use cases (e.g. style, domain).

The process combines broad language knowledge with precise adaptation to specific needs, smoothly and effectively.

quality control of generation: temperature, top-k and top-p

The quality and “style” of generation are most often controlled by decoding settings such as temperature, top-k and top-p. Temperature regulates the level of randomness: low (e.g. 0.1–0.3) gives more predictable, cautious responses, while higher (e.g. 0.8–1.2) increases variety but also raises the risk of mistakes. Top-k narrows the choice to the k most probable tokens, while top-p (nucleus sampling) narrows it to the smallest set of tokens with a combined probability of p (e.g. 0.9). Overly permissive settings can make the model more likely to reach for rare tokens, which may be perceived as “odd vocabulary” or a less stable tone.

For factual tasks and data extraction, a low temperature usually works better, because it reduces randomness. In applications, top_p in the region of 0.9–0.95 and a moderate temperature are often used, but the choice depends on the goal (e.g. creative writing vs precise answers). If you need a consistent format, treat these parameters as “dials” that set the balance between predictability and variety. In practice, it is worth comparing settings on the same input examples to assess at what point the risk of undesirable deviations in the content starts to rise.

• Temperature: lower = more predictable; higher = more varied and a greater risk of errors.
• Top-k: choice limited to the k most probable tokens.
• Top-p: choice from a pool of tokens with a combined probability of p (e.g. 0.9), which stabilises sampling.

applications of generative AI in everyday life and business

Generative AI most often supports language-based and “assistant-like” tasks, such as writing emails, summarising documents, creating proposals, generating campaign ideas, analysing feedback and helping with programming. The best results appear where a person verifies the outcome, because the model can write fluently, but it does not have a built-in guarantee of factual accuracy. In practice, it can, for example, turn a long report into a bullet-point list of risks and recommendations, which speeds up decision-making work. Treat the output as a draft: it helps you get started and organises the material, but it needs checking in critical areas.

Generative AI works more effectively when it is given specific input data and clear success criteria, rather than a vague “write something about…”. If the answer misses the point, a common cause is a lack of numbers, constraints, source text or consistent context. A simple workflow helps: goal → data → requirements → example of the expected format, and for larger tasks, first ask for a plan and only then proceed step by step. In long projects, break the work into modules (analysis, draft, version, correction) and at the end ask for a list of assumptions to verify.

recognition in generative AI

Recognition in generative AI comes down mainly to spotting moments when the model sounds convincing, but may provide false or biased content. Hallucinations appear when the model optimises for fluency and coherence of the response rather than factual accuracy, which means it can “pretend” to be citing or explaining without any real verification. The model does not have an external fact-checking mechanism unless the application connects it to tools (e.g. a search engine), so in critical tasks human oversight is essential. If the response lacks verifiable specifics or contains diverging numbers and “too perfect” publication titles, treat it as a sign of hallucination risk.

Recognition also includes assessing safety and privacy risks, because the model may reproduce stereotypes present in the training data and may become a target of jailbreak and prompt injection attacks. Prompt injection involves inserting instructions into the content (e.g. in a document attached in RAG) such as “ignore previous instructions”, which often results from insufficient separation of instructions from data. From the user’s point of view, what you paste also matters: in public tools, sensitive data should not be disclosed (e.g. PESEL, card numbers, passwords, full medical data, trade secrets), because conversations may be logged for quality analysis depending on the provider and settings. In practice, the safest assumption is that everything you paste may be stored in the system logs.

• Verify the “hard” elements: numbers, dates, proper nouns and whether the model provides details that can be independently checked.
• Ask for sources or quotes from the provided documents, and if in doubt ask for risky fragments or alternative variants to be identified.
• Be cautious of content that may reinforce bias and assess whether the description is based on stereotypical associations.
• Do not paste sensitive data; in companies, anonymisation and DLP policies are used, among other things.
• When it comes to code, treat the output as a suggestion: models may generate vulnerabilities (e.g. SQL injection, lack of input validation), so tests, linters, scanners and review are needed.