Large Language Models (LLMs)

Large Language Models (LLMs) are deep neural networks trained on large collections of text, code, and other data to understand and generate language. They power chat assistants, code-generation tools, document processing, semantic search, retrieval-augmented generation, AI agents, and many other modern AI applications.

At their core, LLMs perform a surprisingly simple task: given a sequence of tokens, predict what token should come next. Repeating that prediction produces complete sentences, code, structured data, and longer responses. The complexity comes from the enormous neural networks, training datasets, context processing, and inference infrastructure behind those predictions.

Table of Contents

• What an LLM Actually Does
  • Text Becomes Tokens
  • Tokens Become Vectors
  • The Model Predicts the Next Token
• How LLMs Understand Context
• How LLMs Are Trained
  • Pretraining
  • Post-Training
• Why LLMs Can Perform Many Tasks
• LLM Inference and Generation
  • Sampling and Temperature
  • Context and Output Cost
• LLMs Do Not Work Like Databases
• Building Applications with LLMs
  • Structured Output
  • Failure Handling
• Production Trade-Offs
• Conclusion
  • More Articles to Read

What an LLM Actually Does

An LLM accepts a sequence of input tokens and calculates probabilities for possible tokens that could follow them. One token is selected, appended to the sequence, and the process repeats until generation stops.

This mechanism is called autoregressive generation in many widely used language models.

For example, given:

The capital of France is →

the model might assign high probability to the token representing Paris. After selecting it, the generated sequence becomes part of the context used to predict the following token.

The model does not contain an explicit rule such as:

if prompt == "The capital of France is":
    return "Paris"

Instead, the answer emerges from patterns encoded in billions of learned parameters.

Text Becomes Tokens

LLMs do not process ordinary text directly. A tokenizer converts text into smaller units called tokens and maps each token to a numerical identifier.

A token may represent a complete word, part of a word, punctuation, whitespace, or another frequently occurring sequence. The exact segmentation depends on the tokenizer.

Conceptually:

"Distributed systems are difficult." → tokens → token IDs

The number of tokens matters because LLM APIs, context limits, memory requirements, latency, and pricing are commonly based partly on token counts rather than character or word counts.

Tokenization and its architectural consequences are covered in more detail in AI Tokens and Context Windows.

Tokens Become Vectors

A token ID alone carries little useful meaning. The model converts each token into a numerical vector called an embedding.

An embedding may contain hundreds or thousands of dimensions. Those dimensions allow the network to represent relationships learned during training.

Instead of processing a token as:

database

the neural network operates on a vector conceptually similar to:

[0.18, -0.42, 0.71, 0.09, ...]

These internal representations evolve as they pass through the network. The representation of a word can therefore depend on surrounding context rather than having only one fixed interpretation.

Embeddings are also used independently in semantic search and retrieval systems. AI Embeddings covers that application in depth.

The Model Predicts the Next Token

After processing the current context, the model produces scores for possible next tokens. These scores are converted into a probability distribution.

A simplified result might look like:

{
  "Paris": 0.91,
  "Lyon": 0.02,
  "France": 0.01,
  "London": 0.003
}

Generation selects a token from this distribution, adds it to the context, and performs another prediction.

A response containing hundreds of tokens therefore requires hundreds of sequential generation steps. This explains an important performance property of LLMs: generating long output is computationally expensive and inherently sequential for autoregressive models.

How LLMs Understand Context

The meaning of language depends heavily on relationships between words. In the sentence The database was overloaded, so it rejected the request, interpreting it requires understanding the surrounding sequence.

Modern LLMs primarily use the transformer architecture, which relies on attention mechanisms to model relationships between tokens.

Attention allows the representation of one token to incorporate information from other relevant tokens in the available context. This makes it possible to connect references, follow instructions, analyze code dependencies, and reason over information located elsewhere in a prompt.

Multiple transformer layers repeatedly transform these contextual representations. Earlier and later layers can learn different types of relationships, producing increasingly useful internal representations.

The architecture is discussed separately in Transformers and Attention in AI.

Context is limited. Every model has a context window defining how much tokenized information can participate in one inference request. Depending on the system, this may include system instructions, conversation history, retrieved documents, tool results, and generated output.

A larger context window does not mean every included fact will influence the answer equally well. Long contexts increase computation and can make relevant information harder for the model to use effectively. Application architecture should therefore select useful context rather than blindly sending every available document or conversation message.

How LLMs Are Trained

LLM development usually involves multiple training stages. The exact process differs across models, but it is useful to separate pretraining, which teaches broad language and knowledge patterns, from post-training, which adapts model behavior for useful interaction.

Pretraining

During pretraining, the model processes very large datasets containing text, code, and other supported forms of data. For an autoregressive language model, the training objective commonly involves predicting the next token.

Suppose the training sequence contains:

A database index improves query performance.

The model repeatedly receives preceding tokens and learns to assign greater probability to the expected next token.

If its prediction is poor, a loss function measures the error. Backpropagation computes gradients, and an optimizer adjusts the network's parameters.

This happens across enormous numbers of token sequences. Over time, parameters encode statistical patterns involving grammar, concepts, code structure, relationships, writing styles, and other information present in the training data.

The same fundamental training mechanics used by smaller neural networks apply at much larger scale. Neural Networks and Deep Learning explains loss functions, backpropagation, and gradient-based optimization.

Pretraining large models is infrastructure-intensive. Model parameters, optimizer state, gradients, and intermediate activations may exceed the memory available on a single accelerator, requiring distributed training across many devices.

Post-Training

A pretrained model can predict language without necessarily behaving like a useful assistant. Post-training adapts the model toward desired behaviors such as following instructions, producing structured responses, refusing unsafe requests, or interacting with tools.

One approach is supervised fine-tuning, where the model trains on examples containing desired inputs and outputs.

Other techniques use human or model-generated preference information to teach the model which responses are more desirable. The implementation differs among model developers, but the purpose is similar: transform a general pretrained model into one whose behavior better matches intended applications.

Fine-tuning can also adapt an existing model to specialized domains or tasks without repeating full pretraining. AI Model Training and Fine-Tuning covers these approaches and their trade-offs.

Why LLMs Can Perform Many Tasks

Traditional machine-learning models are often built for one narrowly defined task. A fraud classifier predicts fraud; a delivery model estimates delivery time.

LLMs behave differently because large-scale language training exposes the model to many patterns and tasks expressed through language. The same model can potentially summarize an incident report, classify a support ticket, generate SQL, explain source code, extract fields from a document, or rewrite text.

The requested task is often specified dynamically through a prompt rather than through model retraining.

Classify this support request as billing, technical, account, or other.

Request:
"I was charged twice for the same subscription."

The same model can immediately receive a different instruction:

Extract the order ID, carrier, and tracking number from this message.
Return JSON only.

This flexibility is one of the main reasons LLMs changed AI application development. A general-purpose model can become a reusable reasoning and language component inside many workflows.

However, flexibility also reduces predictability. The model is interpreting instructions rather than executing a strictly defined function implementation. Prompt design, output constraints, evaluation, and validation therefore become part of application engineering. AI Prompt Engineering examines these techniques in more detail.

LLM Inference and Generation

When an application sends a prompt to an LLM, the model first processes the supplied input context and then generates output tokens. These two phases have different computational behavior.

Processing the input is often called prefill. The model can process many input tokens in parallel. Output generation, often called decoding, is more sequential because each newly generated token becomes input for predicting the next one.

This is why a response may begin after a noticeable delay and then appear token by token through streaming.

Sampling and Temperature

The model calculates probabilities rather than directly producing a guaranteed next token. A decoding strategy determines how a token is selected from those probabilities.

Always choosing the highest-probability token makes generation more deterministic, although exact reproducibility can still depend on the model and serving implementation.

Sampling allows lower-probability alternatives to be selected. Temperature is commonly used to adjust how concentrated the probability distribution is before sampling.

Lower temperature generally favors higher-probability tokens and produces more conservative output. Higher temperature allows more alternatives and can increase variation.

Temperature is not a correctness control. Lowering it can reduce variation but does not make unsupported information true.

Context and Output Cost

LLM requests can become expensive as input and output grow. Long prompts require more context processing, while long responses require more sequential decoding steps.

Suppose an application sends 50,000 tokens of documentation to answer a question requiring only one paragraph. Most of those tokens may be irrelevant, yet they still consume model capacity and can increase latency and cost.

Retrieval systems address this problem by selecting a smaller set of relevant documents or passages before invoking the model. This architecture is commonly known as retrieval-augmented generation (RAG). RAG (Retrieval-Augmented Generation) covers the complete pattern.

Output length also matters. A service generating 2,000 tokens per request generally consumes more inference capacity than one generating 100 tokens. Limits should therefore reflect the actual product requirement rather than simply allowing the model's maximum output size.

LLMs Do Not Work Like Databases

One of the most important misconceptions about LLMs is that they operate as databases containing retrievable records of everything seen during training.

Training changes numerical parameters. It does not normally create a table where a fact can be looked up by primary key. When a model answers a factual question, it generates tokens based on learned statistical patterns.

This has several practical consequences.

• Knowledge may be incomplete. The necessary information may not have been sufficiently represented during training.
• Knowledge can become outdated. Model parameters do not automatically update when the external world changes.
• Exact recall is not guaranteed. Learned patterns are not equivalent to authoritative records.
• Plausible answers can be wrong. The generation mechanism optimizes likely output, not factual database retrieval.

If an application needs the current balance of account 84217, the model should not be expected to remember it. The application should query the authoritative database and provide the result to the model only when language processing is useful.

The same rule applies to inventory, permissions, prices, shipment states, customer records, configuration, and other operational data.

Use databases and APIs as sources of truth; use LLMs to interpret, transform, reason over, or communicate information.

When models produce confident but unsupported information, the behavior is commonly described as hallucination. AI Hallucinations covers the causes and mitigation strategies.

Building Applications with LLMs

A production LLM application is much more than a prompt sent directly from a frontend. Conventional application infrastructure should control what information reaches the model, what outputs are accepted, and what actions are permitted.

A typical backend may authenticate the request, retrieve application data, construct model context, invoke the model, validate the response, record observability data, and return a controlled result.

For example, a support system might ask an LLM to classify an incoming ticket:

from typing import Literal

from pydantic import BaseModel


class TicketClassification(BaseModel):
    category: Literal["billing", "technical", "account", "other"]
    priority: Literal["low", "normal", "high"]


def classify_ticket(text: str) -> TicketClassification:
    response = llm.generate(
        task="Classify the support ticket",
        input=text,
        output_schema=TicketClassification.model_json_schema(),
        timeout_seconds=5,
    )

    return TicketClassification.model_validate(response)

The application does not accept arbitrary text and assume it is valid. The model receives a constrained task, and its output passes through a schema before entering the rest of the system.

Structured Output

Many backend use cases need data rather than prose. Classification, extraction, routing, and tool invocation are easier to integrate when the model returns structured output.

A shipping-document extractor might produce:

{
  "carrier": "carrier_a",
  "tracking_number": "TRK123456",
  "service": "express"
}

Schema validation can ensure that required fields exist and have the expected types. Business validation must still check whether the carrier exists, the tracking number has an acceptable format, and the requested service is valid.

Valid JSON is not the same as valid business data.

Failure Handling

LLM calls should be treated as remote operations that can fail. Failure modes include timeouts, provider errors, rate limits, malformed output, context-limit errors, safety refusals, and responses that pass schema validation but remain semantically incorrect.

The application should decide what each failure means for the caller.

A support-ticket classifier can safely fall back to an unclassified queue. A document extractor might request manual review. A customer-facing assistant can return a reduced feature set when the model provider is unavailable.

Retries should be bounded. Retrying an overloaded model endpoint aggressively can increase queue depth, cost, and latency while making the outage worse.

Operations with side effects require even stronger boundaries. If an LLM can request a refund, modify an account, or execute infrastructure actions, conventional authorization and validation must run after the model proposes the action and before anything executes. AI Tool Calling covers this architecture in depth.

Production Trade-Offs

Choosing an LLM is a multidimensional system-design decision. Model quality matters, but so do latency, throughput, context capacity, availability, privacy, and cost.

Model selection should therefore be task-specific. Using the largest available model for every operation may increase cost and latency without producing meaningful quality improvements.

A common production architecture routes workloads according to complexity. Simple classification or extraction can use a smaller model, while difficult reasoning tasks use a more capable model. Some requests can avoid LLM inference entirely through caching, deterministic rules, search, or ordinary database operations.

Monitoring should include both infrastructure and model-level signals. Useful metrics include time to first token, total response latency, input tokens, output tokens, tokens generated per second, request rate, timeout rate, provider errors, structured-output validation failures, cost per request, fallback rate, and task-specific quality scores.

Quality should be evaluated against representative datasets rather than a few manually selected prompts. A model or prompt change that appears better in several examples can still regress extraction accuracy, increase hallucinations, or break uncommon input formats.

Version prompts, model identifiers, retrieval configuration, and evaluation results alongside application releases. Without this information, investigating why AI behavior changed between deployments becomes difficult.

Finally, LLM output should never receive more authority than the surrounding application can safely control. Probabilistic reasoning belongs inside deterministic security, validation, authorization, and reliability boundaries.

Conclusion

Large language models are deep neural networks that process tokenized context and repeatedly predict the next token. Embeddings represent tokens numerically, transformer layers use attention to build contextual representations, and large-scale training teaches statistical patterns that support many language and reasoning tasks.

Their flexibility makes LLMs powerful application components, but they are not databases, deterministic functions, or authoritative sources of truth. Production systems must account for probabilistic output, context limits, inference latency, cost, failures, and hallucinations. The strongest LLM architecture combines model capabilities with conventional software that supplies trusted data, validates outputs, limits authority, and measures behavior continuously.

More Articles to Read

• Neural Networks and Deep Learning
• Transformers and Attention in AI
• AI Tokens and Context Windows
• AI Prompt Engineering
• RAG (Retrieval-Augmented Generation)