How Do Large Language Models Work?

Large Language Models (LLMs) can generate text, explain code, summarize documents, answer questions, translate languages, and perform many other language-related tasks. From the outside, an LLM can appear to understand a request and formulate an answer almost like a human. Internally, however, the process is based on numerical representations, neural network computations, probability distributions, and repeated token prediction.

Understanding how LLMs work is useful for anyone building AI-powered applications. It explains why prompts matter, why context windows have limits, why longer inputs can increase costs, why models sometimes hallucinate, why responses can vary, and why techniques such as retrieval-augmented generation, structured outputs, and tool calling are useful.

The basic process can be summarized as: text is converted into tokens, tokens are transformed into numerical representations, transformer layers process those representations, the model calculates probabilities for possible next tokens, and a decoding process selects tokens that form the final response. The same process is repeated throughout generation.

How Does an LLM Work?

A modern LLM usually works as a large transformer-based neural network. It receives a sequence of tokens as input and processes them through many layers. The result is a probability distribution over possible next tokens.

• Text is converted into tokens.
• Tokens are mapped to numerical representations.
• Positional information is incorporated so the model can represent token order.
• Transformer layers process the representations.
• Attention mechanisms allow tokens to interact with relevant parts of the context.
• The model produces scores for possible next tokens.
• The scores are converted into probabilities.
• A decoding strategy selects the next token.
• The selected token is added to the sequence.
• The process repeats until generation stops.

The LLM Pipeline

A useful way to visualize the complete process is as a pipeline from the user's text to the generated response.

User input
    ↓
Tokenizer
    ↓
Token IDs
    ↓
Token embeddings + positional information
    ↓
Transformer layers
    ↓
Attention + feed-forward processing
    ↓
Output representations
    ↓
Logits
    ↓
Probability distribution
    ↓
Decoding / token selection
    ↓
Next token
    ↓
Repeat
    ↓
Final response

Each stage solves a different problem. Tokenization converts human-readable text into units the model can process. Embeddings represent those units numerically. Transformer layers transform the representations while modeling relationships between tokens. Finally, the output is converted into probabilities from which the next token is selected.

Step 1: Tokenization

The first step is tokenization. A tokenizer converts input text into a sequence of tokens. Tokens are not necessarily complete words. Depending on the tokenizer, they can represent complete words, parts of words, punctuation, whitespace, or frequently occurring character sequences.

Input:
How do language models work?

Conceptual token sequence:
[How] [ do] [ language] [ models] [ work] [?]

The exact tokenization depends on the tokenizer used by a particular model, so the sequence above is only illustrative. After tokenization, each token is represented by an integer ID from the model's vocabulary.

Text tokens:
[How] [ do] [ language] [ models]

Token IDs:
[1532] [531] [4821] [9073]

The IDs themselves do not contain semantic meaning. They are indexes into the model's vocabulary. The model then uses these IDs to retrieve or construct numerical vector representations called embeddings.

Why Tokenization Matters

Tokenization affects context length, latency, and cost. If a piece of text requires more tokens, the model has to process a longer sequence. API pricing is also commonly based on input and output tokens.

Tokenization can vary considerably between languages. A tokenizer optimized around common English text may represent English efficiently while requiring more tokens for some other languages or specialized terminology.

Step 2: Token Embeddings

Neural networks operate on numerical values rather than raw token IDs. An embedding layer maps each token ID to a vector containing many numerical values.

Token ID
   ↓
Embedding lookup
   ↓
[0.12, -0.48, 0.73, 0.09, ...]

These vectors are learned during training. Tokens that appear in related contexts can develop representations that encode useful similarities and relationships. However, the meaning of a token is not fixed in isolation. The transformer's later layers modify representations according to the surrounding context.

For example, the word "bank" can refer to a financial institution or the side of a river. The initial token representation does not need to permanently choose one meaning. Transformer processing can use surrounding tokens to construct a context-dependent representation.

Step 3: Positional Information

Language depends heavily on order. The sentences "The dog chased the cat" and "The cat chased the dog" contain the same words but have different meanings. A transformer therefore needs a way to represent where tokens occur in the sequence.

Transformer architectures use positional information in different ways. Some use positional embeddings, while modern architectures may use techniques such as rotary positional embeddings. The implementation varies between models, but the purpose is similar: provide information about token positions and relationships between positions.

Step 4: Transformer Processing

After token representations and positional information are prepared, they pass through the transformer's layers. A large language model can contain many such layers stacked together.

Each layer transforms the representations and allows information to flow between different positions. Two of the most important components are attention mechanisms and feed-forward neural networks.

What Is Self-Attention?

Self-attention is one of the defining mechanisms of transformer models. It allows each token position to consider information from other positions in the same sequence.

Suppose the model receives the sentence "The developer fixed the bug because it was causing errors." To process "it," the model can use relationships with other tokens in the sequence. Attention provides a mechanism for calculating how strongly different positions should influence one another.

The attention calculation is commonly described using queries, keys, and values. Each token representation is transformed into these components. Queries are compared with keys to determine attention weights, and the corresponding values are combined according to those weights.

Input representations
        ↓
   Queries / Keys / Values
        ↓
Attention scores
        ↓
Normalized attention weights
        ↓
Weighted combination of values
        ↓
Updated representations

This process allows information from different parts of the context to be incorporated dynamically. It is one of the main reasons transformers are effective at modeling long-range relationships in sequences.

Causal Attention in Language Models

Autoregressive language models generally use causal attention. This means that when predicting a token, the model cannot use future tokens that have not yet been generated.

Sequence:
The developer fixed the bug

When predicting "fixed":
The        ✓
developer  ✓
fixed      being predicted
The future tokens are not visible.

A causal attention mask prevents a position from attending to later positions during training. This makes the training objective consistent with the generation process, where future tokens are unknown at the time each token is generated.

Multi-Head Attention

Transformers commonly use multi-head attention. Instead of performing one attention calculation, the model uses multiple attention heads that can learn different types of relationships.

One attention head might become useful for certain syntactic relationships, another may capture relationships across distant parts of a sequence, and another may respond to different semantic patterns. The model does not receive explicit instructions assigning these roles; they emerge through training.

Feed-Forward Networks

Attention is only one major component of a transformer layer. Transformer blocks also contain feed-forward neural networks, sometimes called multilayer perceptrons.

A feed-forward network applies learned transformations independently to each sequence position after attention has mixed information across positions. Nonlinear activation functions allow the network to represent complex transformations rather than only simple linear relationships.

A simplified transformer layer can therefore be viewed as repeatedly combining information between tokens through attention and transforming the resulting representations through feed-forward networks.

Residual Connections and Normalization

Deep transformer networks contain many layers, so training them requires architectural techniques that help maintain stable information and gradient flow. Residual connections allow a layer's input to be combined with its transformed output.

Normalization layers help control the numerical scale of activations. Different transformer architectures use somewhat different normalization placements and implementations, but normalization and residual pathways are important parts of modern deep transformer designs.

Step 5: Producing Next-Token Scores

After the input passes through the transformer layers, the resulting representation is used to calculate scores for possible next tokens. These raw scores are commonly called logits.

Transformer output
        ↓
      Logits
        ↓
Token A:  4.2
Token B:  2.1
Token C:  0.7
Token D: -1.3
        ↓
   Probability distribution

The model's vocabulary can contain a very large number of possible tokens. The output layer produces a score for each candidate token. These scores are then converted into probabilities using a function such as softmax.

What Is Softmax?

Softmax converts a collection of numerical scores into a probability distribution. The resulting probabilities are positive and sum to one.

Logits:
A = 4.2
B = 2.1
C = 0.7
D = -1.3

After softmax:
A = high probability
B = lower probability
C = lower probability
D = very low probability

The model is therefore not simply choosing between "correct" and "incorrect" answers. At each generation step, it produces a distribution over possible next tokens. The decoding strategy determines how one token is selected from that distribution.

Step 6: Selecting the Next Token

Once probabilities have been calculated, the system must choose a token. The simplest strategy is greedy decoding, which selects the token with the highest probability.

Other decoding strategies can introduce controlled randomness. Sampling can select among likely tokens according to their probabilities. Temperature can modify the distribution before sampling, while methods such as top-k or top-p restrict the candidates considered during sampling.

Step 7: Autoregressive Generation

After a token is selected, it is added to the sequence. The model then processes the updated context to predict another token.

Prompt:
The weather today is

Step 1 → "sunny"

Context:
The weather today is sunny

Step 2 → ","

Context:
The weather today is sunny,

Step 3 → "so"

Context:
The weather today is sunny, so

Step 4 → "we"

...and the process continues.

This repeated process is called autoregressive generation. Each generated token becomes part of the context for subsequent predictions.

Why Does Generation Take Time?

Autoregressive generation is one reason text generation has an inherently sequential component. Although modern inference systems use many optimizations and can process parts of the computation efficiently, generating a response still involves repeatedly producing new tokens.

The longer the requested response, the more generation steps are required. This is one reason output token count affects latency and cost in many hosted LLM APIs.

How Are LLMs Trained?

The generation process described above is possible because the model has previously been trained. Training is the process of adjusting the model's parameters so that its predictions become increasingly useful on its training objective.

Next-Token Prediction During Training

A common training objective for an autoregressive language model is next-token prediction. The model receives a sequence and attempts to predict each next token.

Training text:
The server returned an error

Input:
The

Target:
server

Input:
The server

Target:
returned

Input:
The server returned

Target:
an

In practice, training systems can predict many positions in a sequence in parallel rather than literally running one forward pass for every token. A causal mask ensures that each position only uses information that would have been available when making that prediction.

Loss Function

The model's predictions are compared with the expected tokens using a loss function. For language modeling, cross-entropy loss is commonly used.

If the correct next token receives a high probability, the loss is relatively low. If the model assigns the correct token a very low probability, the loss is higher. Training attempts to reduce this loss across a large number of examples.

Backpropagation and Optimization

After calculating the loss, backpropagation determines how the model's parameters contributed to the error. An optimizer then uses the resulting gradients to update the parameters.

Training data
    ↓
Forward pass
    ↓
Predictions
    ↓
Loss calculation
    ↓
Backpropagation
    ↓
Gradients
    ↓
Optimizer update
    ↓
Updated parameters
    ↓
Repeat

This process is repeated over enormous numbers of training examples. Over time, the model's parameters adjust so that the network becomes better at predicting patterns in the training data.

Why Does Predicting the Next Token Produce Useful Abilities?

At first glance, predicting the next token may sound like a narrow task. In reality, doing this well across diverse text requires the model to learn many relationships. To predict a token accurately, the model can benefit from information about syntax, semantics, entities, facts, discourse structure, programming patterns, and relationships between concepts.

As model scale, training data, and training methods improve, increasingly sophisticated capabilities can emerge from this objective. The model is not necessarily given a separate explicit rule for every capability. Instead, many useful representations are learned as part of optimizing the prediction task.

Pretraining vs Inference

This distinction is important. When you send a prompt to an ordinary hosted LLM, you are normally not teaching the model permanently. The model is using its existing parameters to process your request. Conversation history can affect the current context, but that is different from updating the underlying model weights.

What Happens to Previous Messages in a Chat?

In a conversational application, previous messages can be included in the model's context along with the latest user message. The model then processes the available conversation as part of the input sequence.

This is one reason conversations can become expensive or eventually exceed context limits as they grow. More historical messages mean more input tokens that the application may need to send and the model needs to process.

What Is the Context Window?

The context window is the amount of tokenized information a model can process as context for a request. Depending on the model and service, this can include system instructions, conversation history, user input, retrieved documents, tool results, and other data.

The context window should not be confused with long-term memory. Context is information available to the model for a particular computation. Model knowledge is represented through learned parameters, while application memory may be stored separately in a database or another system.

What Is the Difference Between Parameters and Context?

Why Can LLMs Answer Questions They Were Not Explicitly Programmed to Answer?

Traditional software usually requires developers to write explicit logic for a specific task. LLMs can generalize from patterns learned during training. If the model has learned representations that are useful across related tasks, it can apply those representations to new prompts.

For example, a model trained on many examples of programming questions may be able to explain a programming language feature even if that exact question never appeared in the training data. It combines learned patterns and the current context to produce a new response.

This generalization is powerful, but it is also one reason reliability cannot be assumed. The model can generalize incorrectly and produce a plausible answer that does not correspond to reality.

Why Do LLMs Hallucinate?

The core training objective is prediction, not guaranteed factual verification. The model learns to produce likely sequences based on patterns in its training data and current context. If a prompt does not provide enough information, the model may still generate a plausible continuation.

This can lead to fabricated facts, nonexistent references, incorrect technical details, invented APIs, or other unsupported claims. The response can be grammatically correct and highly convincing while still being wrong.

How Does RAG Change the Process?

Retrieval-Augmented Generation, or RAG, adds an external retrieval step before generation. Instead of relying entirely on the model's learned parameters, an application retrieves relevant information and places it into the model's context.

User question
      ↓
Search / retrieval
      ↓
Relevant documents
      ↓
Construct prompt with retrieved context
      ↓
LLM
      ↓
Generated answer

This approach is useful when the application needs information that is current, private, domain-specific, or too large to rely on entirely through model training.

How Do LLMs Use External Tools?

An LLM cannot automatically perform every real-world action just because it can generate text. AI applications can give models access to external tools such as databases, calculators, search systems, APIs, code execution environments, or business systems.

In a tool-using system, the model can generate a structured request indicating which tool should be called and with which arguments. The application executes the tool and returns its result to the model, which can then use that information to produce a final response.

User request
    ↓
LLM decides a tool is useful
    ↓
Structured tool call
    ↓
Application executes tool
    ↓
Tool result
    ↓
LLM processes result
    ↓
Final response

This architecture is important because it separates language generation from deterministic operations. For example, a calculator can perform exact arithmetic while the LLM explains the result in natural language.

Why Are LLMs Good at Code?

Source code is another type of structured token sequence. Programming languages contain syntax, repeated patterns, naming conventions, documentation, and relationships between code constructs. Models trained on large collections of code can learn many of these patterns.

When generating code, the same next-token prediction mechanism is used. The model receives a prompt containing natural language, source code, or both, and predicts a sequence of tokens that forms the requested output.

However, generated code is not guaranteed to compile, pass tests, follow project-specific conventions, or be secure. Reliable coding assistants therefore benefit from tools such as compilers, linters, test runners, documentation retrieval, and repository-aware context.

What Determines LLM Quality?

LLM quality depends on many factors rather than a single parameter. Model architecture, training data, training compute, parameter count, optimization methods, post-training, context handling, inference techniques, and application design can all affect results.

Why Does a Larger Model Usually Need More Compute?

Larger models contain more parameters and therefore require more numerical operations and memory during training and inference. Processing longer contexts can also increase computational requirements.

However, model size alone is not a complete measure of capability. Training data quality, architecture, optimization, post-training, and inference efficiency can allow a smaller model to outperform a larger model on particular tasks.

Inference Optimization

Serving LLMs efficiently is a major engineering challenge. AI providers and developers use techniques such as batching, optimized kernels, quantization, caching, parallelism, and specialized hardware to reduce latency and cost.

For autoregressive generation, the system can also cache intermediate attention-related information from previous tokens. This avoids recomputing certain information from scratch for every newly generated token and is an important part of efficient transformer inference.

What Is the KV Cache?

During transformer inference, attention uses key and value representations. When generating tokens sequentially, previously computed key and value information can often be stored in a key-value cache, commonly called the KV cache.

The KV cache allows the model to reuse information from earlier tokens instead of recalculating all of it for every generation step. This can significantly improve generation efficiency, although the cache itself consumes memory and grows with the amount of context being processed.

Why Does Context Length Affect Memory?

A longer context means more token representations and, during generation, a larger amount of cached attention information. As context length grows, memory requirements can become a significant constraint for inference systems.

This is one reason long-context models require careful engineering. A large context window is useful, but simply increasing the limit does not make every application better. Developers should still retrieve relevant information, remove unnecessary history, and design prompts efficiently.

Why Can an LLM Lose Track of Information?

Even when a model technically supports a large context, information quality can vary depending on where and how information appears in the prompt. Extremely long inputs can contain irrelevant or conflicting material, making it harder for the model to focus on the most important information.

This is sometimes discussed in terms of context utilization or long-context performance. Developers should therefore distinguish between the maximum context window advertised by a model and the amount of context that produces consistently useful results for a particular application.

How Do LLMs Learn Different Languages?

A multilingual LLM can learn relationships across multiple languages when its training data contains those languages. The same neural network can represent tokens from different languages within a shared parameter space.

Performance can vary between languages because training data volume, quality, tokenization efficiency, and language-specific characteristics differ. A model that performs extremely well in one language may not perform equally well in another.

What Is the Difference Between an LLM and a Chatbot?

An LLM is the underlying model, while a chatbot is an application or interface that uses a model to communicate with users. A chatbot can combine an LLM with conversation history, system instructions, retrieval, tools, authentication, databases, moderation, and other software components.

A Complete Example

Consider a user asking an AI assistant: "Explain why this JavaScript function returns undefined." A production application may perform several steps before the user sees an answer.

• The application receives the user's message.
• The message is combined with relevant conversation history and system instructions.
• The resulting text is tokenized.
• Token IDs are converted into embeddings.
• The transformer processes the sequence using attention and feed-forward layers.
• The model predicts probabilities for the next token.
• A decoding method selects a token.
• The selected token is added to the sequence.
• The process repeats until the response is complete.
• The application may validate, format, or stream the generated response to the user.

If the assistant also has access to a code execution tool, it could potentially analyze or run the relevant function before producing the final explanation. In that architecture, the LLM handles language and reasoning-like generation while conventional software provides exact execution and verification.

LLMs Are Predictive Models, Not Databases

A common misconception is that an LLM contains a searchable database of everything it learned. Its learned information is instead encoded in the numerical parameters produced through training. Although models can memorize some information, generating an answer is fundamentally a prediction process.

This distinction explains why an LLM can sometimes answer a question correctly without being able to provide the exact source of its knowledge. It also explains why retrieval systems are valuable when an application needs traceable, current, or authoritative information.

LLMs and Deterministic Software

The strengths of LLMs and traditional software are complementary. LLMs are flexible and effective at handling natural language, while deterministic software is better suited to operations that require exact and predictable results.

• Use an LLM for natural-language interpretation and generation.
• Use ordinary code for exact calculations.
• Use databases for authoritative structured information.
• Use APIs for current external data.
• Use retrieval for domain-specific knowledge.
• Use validators for structured model output.
• Use authentication and authorization for access control.
• Use tests and monitoring to evaluate application behavior.

Key Concepts to Remember

• LLMs process tokens rather than raw text.
• Token IDs are transformed into numerical representations called embeddings.
• Transformers process token representations through multiple layers.
• Self-attention allows tokens to incorporate information from other positions.
• Causal attention prevents autoregressive models from using future tokens when predicting the next token.
• The model produces logits for possible next tokens.
• Softmax can convert logits into a probability distribution.
• Decoding determines which token is selected.
• Autoregressive generation repeats this process token by token.
• Training adjusts model parameters to improve predictions.
• Inference uses the trained parameters to process new requests.
• Context provides information for the current request but is different from learned model parameters.
• RAG and external tools can provide information and capabilities that are not contained directly in the model.
• Generated output should be validated when correctness is important.

Frequently Asked Questions

Helpful AI Tools

AI and developer tools can help you experiment with language models, inspect prompts, work with structured outputs, estimate token usage, test generated content, and build applications around LLM APIs. They are especially useful for understanding how changes in prompts, context, and generation settings affect model behavior.

Conclusion

Large Language Models work by transforming tokenized input through a deep neural network, typically based on the transformer architecture. Tokenization converts text into manageable units, embeddings represent those units numerically, and transformer layers use attention and feed-forward networks to build increasingly useful contextual representations.

At the end of the network, the model produces scores for possible next tokens. These scores are converted into probabilities, a decoding strategy selects the next token, and the process repeats. This simple autoregressive mechanism is the foundation of modern text generation.

The capabilities of LLMs come from the scale and quality of their training, their architecture, learned parameters, and additional techniques used during post-training and inference. However, an LLM is only one part of a reliable AI application. Retrieval, external tools, deterministic code, validation, security, and monitoring can all be necessary when building production systems.