How LLMs Generate Text: Tokens, Probabilities, and Sampling Mechanisms
Large language models generate text by predicting the probability distribution of the next token, rather than thinking through a complete answer and outputting it all at once like a human would.
Although users see naturally flowing sentences, behind the scenes, each output results from extensive mathematical calculations and probability-based selections. The Transformer network analyzes the relationships between different tokens based on context and generates a probability distribution for the next token. The model then selects an output according to a sampling strategy and repeats this process continuously, ultimately forming a complete response.
With the rapid advancement of generative AI, text generation mechanisms have become a core capability of modern AI systems. By 2026, mainstream models have expanded their context windows from a few thousand tokens during the GPT-3 era to hundreds of thousands or even millions, while inference costs keep dropping. According to the Stanford HAI’s "AI Index Report 2026," model inference costs have dropped by several orders of magnitude compared to just a few years ago. This is a key driver behind the rapid development of AI search, AI agents, and enterprise automation systems.
What Is the LLM Text Generation Mechanism?
At its core, large language models don’t form a complete answer in advance like humans do. Instead, they continuously predict the most likely next token. While this process may seem simple, it forms the foundation for all generative AI capabilities. Whether it’s chatbots, code assistants, AI search, or agent workflows, their underlying logic is built on this mechanism.
For example, when you input:
The capital of France is
The model uses knowledge and context it learned during training to calculate the probability of different tokens appearing next. For most models, "Paris" will have a much higher probability than "London" or "Berlin," so the model is more likely to generate "Paris." This process is not about looking up a database or retrieving a pre-stored answer—it’s an ongoing probability prediction.
The strength of this mechanism lies in its generality. Because the model is always performing the same task—predicting the next token—it can handle Q&A, translation, code generation, reasoning, and knowledge retrieval across different contexts. That’s why understanding the text generation mechanism is also the foundation for grasping concepts like AI Tokens, Tokenization, Temperature, Context Window, and prompt engineering.
What Preparation Is Needed Before Text Generation?
For humans, language consists of words and sentences. But for models, all input is ultimately converted into a sequence of tokens. The model can’t directly understand natural language; instead, it operates on digitized tokens. So before text generation can begin, the model must complete a series of preprocessing steps.
First, the input text goes through tokenization. The tokenizer splits sentences into multiple AI tokens—these can be words, subwords, or even character fragments. For example, a standard English sentence might be split into dozens of tokens, each corresponding to an entry in the vocabulary.
Next, the embedding layer converts these discrete tokens into vector representations, allowing the model to learn the semantic relationships between different words. Only after this vectorization step does the data enter the Transformer network for computation. Because of this process, large models actually process high-dimensional vectors, not the visible text users see.
The overall inference flow typically looks like this:
User Prompt↓AI Tokens↓Tokenization↓Embedding↓Transformer Network
Therefore, AI tokens and tokenization are considered the most fundamental components of modern large language models, and they are key factors affecting both cost and context length.
What Does the Transformer Network Actually Compute?
The Transformer is the core architecture of modern large language models. Introduced by Google in the 2017 paper "Attention Is All You Need," the Transformer architecture underpins models like GPT, Claude, Gemini, Llama, and DeepSeek. Compared to traditional recurrent neural networks, Transformers can process the entire context at once, greatly improving training efficiency and long-text processing capabilities.
The core idea of the Transformer is attention. When the model is about to generate the next token, it doesn’t treat all context information equally. Instead, it dynamically calculates the importance of different tokens. For example, in the sentence "The capital of France is," the model focuses on "France" and "capital" rather than giving equal weight to "The" and "is."
This dynamic attention mechanism enables the model to understand long-range dependencies and is a key reason why modern large language models have reasoning capabilities. As context windows expand, the importance of attention grows, forming the foundation for long-text analysis, code generation, and agent workflows.
After passing through multiple layers of attention and feed-forward networks, the Transformer outputs a set of values called logits. However, logits are not probabilities in the strict sense—they are the model’s raw scores for each possible token. The model must then convert these scores into a probability distribution.
How Are Logits, Softmax, and Probability Distributions Generated?
Once the Transformer finishes its calculations, the model obtains a set of logits corresponding to the entire vocabulary. You can think of logits as the model’s preference scores for each candidate token, but these values themselves can’t be directly interpreted as probabilities.
To produce a true probability distribution, the model applies the softmax function to normalize the logits. After softmax transformation, the probabilities of all tokens sum to 100%, allowing the model to determine which token is most likely to appear. For example, in the context "The capital of France is," "Paris" usually receives the highest probability.
This process is at the heart of text generation. The Transformer understands the context, logits provide the raw scores, and softmax converts those scores into probabilities. The sampling mechanism then decides which token to output based on these probabilities, adds it to the context, and repeats the cycle.
A simplified version of this process looks like:
Prompt↓AI Tokens↓Embedding↓Transformer↓Logits↓Softmax↓Probability Distribution↓Sampling↓Next Token
This loop can repeat dozens, hundreds, or even thousands of times until the model meets its stopping criteria and produces a complete response.
How Do Temperature, Top-k, and Top-p Affect Results?
Even after the model generates a probability distribution using the Transformer and softmax, the highest-probability token isn’t always selected directly. To balance stability and creativity, modern large language models typically introduce a sampling mechanism, with temperature, top-k, and top-p being the most common control parameters.
Temperature controls the smoothness of the probability distribution. When temperature is close to 0, the model tends to select the highest-probability token, resulting in more stable and deterministic outputs. This setting is ideal for code generation, mathematical reasoning, and scenarios that require high accuracy. Higher temperatures increase the chance of selecting lower-probability tokens, boosting output diversity and creativity, making it more suitable for writing, brainstorming, and content generation tasks.
Top-k and top-p further restrict the candidate pool. Top-k retains only the top k tokens with the highest probabilities, while top-p dynamically selects a candidate set based on cumulative probability. Compared to the fixed-size top-k, top-p can flexibly adjust the sampling range according to context, which is why many commercial models use it as the default method.
These parameters explain why the same question can yield different answers at different times. For developers, understanding temperature and sampling mechanisms is key to prompt optimization and controlling model behavior.
Why Does Token Count Affect Cost and Speed?
For large language models, tokens are not only the basic units of text processing but also the main factor determining inference cost. Nearly all commercial models—including those from OpenAI, Anthropic, and Google Gemini—use token count as the primary billing metric. The longer the input and the more generated output, the higher the overall cost.
As of 2026, with model capabilities rapidly advancing, the context window has expanded to hundreds of thousands or even millions of tokens. However, a larger context window doesn’t necessarily mean lower inference costs. In fact, because the Transformer’s attention mechanism must analyze all tokens in the context simultaneously, computational complexity increases significantly with context length.
According to the Stanford HAI "AI Index Report 2026," model inference costs have continued to fall over the past few years, dropping by more than 99% compared to the early GPT-3 era for models with similar capabilities. This trend has fueled the growth of AI search, RAG systems, and AI agents, enabling more enterprises to deploy generative AI in production environments.
As a result, optimizing token efficiency has become a crucial aspect of enterprise AI cost management. Shorter prompts, better context management, and more effective model routing strategies can all significantly reduce inference overhead. This is why context window and prompt engineering are attracting increasing attention.
How Is Streaming Output Achieved?
When using ChatGPT, Claude, or Gemini, users often see the model outputting answers word by word. This experience is called streaming output.
Under the hood, streaming output doesn’t mean the model generates the entire answer at once. Instead, it returns each token to the user as soon as it’s generated. The new token is then added to the context, and the model continues with the next round of prediction. By repeating this process, users see the answer unfold in real time.
The main advantage of this design is a significant reduction in perceived wait time. Although complete inference may take several seconds or longer, users can typically see the first token within a few hundred milliseconds, resulting in a better interactive experience. For AI search, code assistants, and real-time dialogue systems, streaming has become the default mode.
As AI agents and complex workflows evolve, low latency and real-time feedback are becoming increasingly important. More enterprises are now focusing on model response time, first token latency (TTFT), and overall inference throughput—metrics that are becoming key performance indicators for models.
What Issues Can Arise During Text Generation?
While probabilistic generation is powerful, it doesn’t guarantee that the model will always provide correct answers. Since the model’s core function is to predict the next token—not to verify facts—hallucination remains a significant challenge for large language models.
When training data is biased, context is insufficient, or sampling parameters are set too high, the model may generate information that sounds plausible but is actually incorrect. Higher temperatures increase output randomness, which can also lead to more repetition, logical inconsistencies, and factual errors.
Large language models are also limited by the context window. When the context is too long, the model may forget earlier information, impacting answer quality. As agent workflows become increasingly complex, ensuring stable long-term memory, state management, and multi-step reasoning has become a key research focus.
As a result, modern AI systems are increasingly relying on RAG, tool use, model routing, and external knowledge bases to enhance model capabilities. More enterprises now recognize that large models are just one part of the AI infrastructure, and that stability, security, and governance are equally important.
Conclusion
At its core, the text generation process of large language models is about continuously predicting the next token and repeating this process. Although users see natural, fluent sentences, each token is generated through multiple steps: tokenization, embedding, Transformer computation, logits, softmax, and sampling.
As models scale up, the generation mechanism is becoming a vital part of modern AI infrastructure. According to the Stanford HAI "AI Index Report 2026," the ongoing drop in inference costs and the expansion of context windows are driving rapid growth in AI search, RAG systems, and AI agents.
Understanding tokens, probability, and sampling mechanisms not only explains how models generate natural language, but also helps developers gain deeper insights into AI tokens, temperature, context window, prompt engineering, and the operating logic of modern AI systems. In a sense, all generative AI capabilities are ultimately built on the simple yet powerful mechanism of "predicting the next token."
FAQ
How do LLMs generate text?
LLMs generate text by predicting the probability distribution of the next token and repeatedly generating tokens to form a complete answer.
What is an AI token?
An AI token is the basic unit of text processed by the model and the smallest computational unit in the text generation process.
What does the temperature parameter do?
The temperature parameter controls the randomness and creativity of the model’s output and affects the stability of generated results.
Why can the same question yield different answers?
The same question can produce different answers mainly because the sampling mechanism and parameters like temperature, top-k, and top-p influence token selection.
Why does token count affect cost?
Token count directly determines the model’s computational load and inference time, making it the primary billing metric for most commercial models.
Do large language models truly understand text?
Large language models do not truly understand text; at their core, they are still predicting the next token based on probability.
