What Is an LLM? How Large Language Models Work

An LLM is a large language model trained to learn relationships within language and use them for tasks such as text generation, summarisation, classification and answering questions. The abbreviation stands for Large Language Model. In a company, the model can become a component of a document assistant, a support tool or an internal application.

To understand how an LLM works, we follow the complete path: text is represented through tokens, the model performs computations using its learned parameters, and the application organises context and uses the result. Documents, internet access and operations within a CRM are additional components connected explicitly.

The relationship with the broader field is explained in our guide to the differences between AI, machine learning and deep learning. Here we focus on the language model and the mechanisms behind a conversation, without confusing the model with the commercial product in which it appears.

What is an LLM, and what does “large” mean?

A language model learns statistical relationships between elements of language. In a neural model, this learning adjusts numerical parameters, also called weights. When given a new input, the model uses its parameters and the information available in the request to calculate an output.

“Large” describes the scale of the model and its development process, but does not provide a universal numerical threshold. Parameter count, data, architecture and the training objective contribute differently to behaviour. A size label does not replace evaluation on a specific task.

In conversational applications, many generative LLMs use decoder-style Transformer architectures to produce continuations of their input sequence. However, language models form a broader family: encoder and encoder–decoder architectures also represent and transform text. The explanation of generation below explicitly concerns the autoregressive mechanism.

LLM, chatbot and application: what is the difference?

The language model performs the processing

The LLM is the component that receives input and produces results. It can be accessed through an API service or hosted on infrastructure managed for the project. These options change resources and operating responsibilities, not the definition of a model.

The chatbot organises the conversation

A chatbot is the interface and dialogue logic through which users make requests. It can use an LLM, fixed rules or a combination. The appearance of a chat window does not, on its own, reveal the model, sources or integrations behind it.

The application determines what can be done

Authentication, roles, the database and approvals belong to the software system. A model producing an answer about an order does not automatically receive permission to change that order. Our guide to chatbots versus AI agents for business explores the difference between conversation and executing steps.

How does the model turn text into tokens?

A token is not equivalent to a word

The tokenizer converts text into elements identified numerically. A token can represent part of a word, a whole word, punctuation or character combinations. Segmentation depends on the tokenizer, language and text; diacritics, product codes and text obtained through optical character recognition can change token counts.

A limit expressed in tokens therefore does not translate into a fixed number of Romanian pages or words. A real project measures representative samples: instructions, questions, documents and responses. A simplified conversion from another language is not enough to size an application.

Token identifiers are converted into numerical representations, called embeddings, which are then processed by the network. Users see text and paragraphs; the model works with representations and computations. The resulting sequence is decoded for display.

What do the Transformer and attention mechanism do?

Relationships between fragments matter for interpretation

A Transformer combines attention layers with other transformations of representations. Attention relates positions within a sequence and computes contributions from available information. Positional information helps the model process the order of elements rather than only their presence.

For example, “a quotation for the installed pump” must be interpreted within the request and the products being discussed. The same linguistic form can require different information in a sales conversation and a technical manual. This illustrates the role of context; it is not a measurement of a model's attention.

In an autoregressive decoder, prediction uses available context and preceding tokens, not the future answer. Attention is a computational mechanism; it is not, by itself, a fact check or evidence that a statement has a source. Those checks are designed around the task.

How is a large language model trained?

Pretraining develops general representations

During pretraining, parameters are adjusted using collections of data for a learning objective. For an autoregressive generative model, a common objective is predicting the next token from context. Other families can reconstruct masked passages or use other objectives.

The learning signal can be derived from the data itself through self-supervised learning. This does not remove selection, filtering and evaluation work. The composition of the collection and the quality of examples influence the language, patterns and information that the model can reproduce or combine.

Post-training adapts behaviour

Later stages can use response examples, instructions and preferences to adjust behaviour. Research on training with human feedback documents the distinction between a model that continues text and one adapted to respond to requests.

This should not be confused with adding a document to a request. A company can build an application using a pretrained model without creating its own network from scratch. A proposal should specify what is configured, what data is added and whether the parameters are actually updated.

How does an LLM generate a response, step by step?

Inference uses the parameters for the current request

The application prepares instructions, the user's message and relevant context. In an autoregressive generative model, the network computes scores for possible continuations. The decoding procedure selects the next token, and the process continues until a stopping condition or the configured limit is reached.

This does not mean the system always selects the highest-scoring token. Sampling strategies and other decoding procedures can be used. In implementations that expose a temperature parameter, it influences continuation selection; it is not a control for factual correctness.

Some systems include intermediate reasoning steps, tool calls and checks before producing a final answer. Coherent text can result from a complex process, but the probability of a continuation is not the same measure as justified confidence in business information.

In this field, a “hallucination” is erroneous or fabricated generated content, even when expressed convincingly. In an answer about a product, it could be a feature absent from the technical specification. Verification compares the claim with its source and explicitly handles missing information rather than assessing fluency alone.

Progressive display, known as streaming, sends fragments to the interface as they become available. Users can start reading before the complete response is finished. The application still distinguishes an output being generated from a completed one, and applies any checks required for an operation before executing it.

For a custom application with AI integration, the result must match the function's format and purpose: a customer-facing answer, an internal summary or proposed form fields. These outputs have different verification criteria.

What is context, and what does the model “remember”?

Parameters, history and application memory are different

Context is the information available to the model in a request: instructions, selected messages, documents or tool results. The context window has a capacity defined in tokens, with model-dependent rules that can include input, output and reasoning tokens.

A long conversation can be selected, summarised or reorganised by the application. Information stored in a database does not automatically enter context. The system must decide what to retrieve for the current request and what to retain for a future interaction.

Parameters result from training; application memory is a software function for storage and retrieval. An assistant using a previously mentioned detail may be relying on supplied history without new training. The policy for retaining and using conversations is a separate, explicit decision.

How does an LLM use company documents and current information?

For procedures, products and changing information, the application can retrieve sources at request time. In a RAG architecture, the question is linked to relevant documents, and selected passages are supplied to the model as working information. RAG stands for Retrieval-Augmented Generation.

Retrieval must account for versions, permissions and user identity. An old document, incomplete passage or file inaccessible to that employee does not become suitable merely because its wording resembles the question. Organising the collection matters before the answer is generated.

Source preparation begins before search. A scanned PDF needs usable text, while tables, headings and product codes must be preserved correctly. Associate each document with a version, an owner and an access scope. This makes it possible to check what information was available to the model, not merely which file existed in a folder.

Our guide to an AI chatbot connected to company documents through RAG covers implementation in depth. The key mechanism here is that a source is retrieved and added to context, not instantly absorbed into model parameters.

Similarly, internet access requires a search or browsing function connected to the system. Track the source actually consulted, its relevant date and the claim it supports. A reference generated in text is checked against the real document; the appearance of a citation does not establish provenance on its own.

How does the model connect to a CRM and other applications?

Through tools or function calls, the model can propose a structured request to software: finding an order, reading stock or preparing an operation. Application code receives the arguments, checks rules and executes only an authorised action. Its result can be sent back to the model to formulate a response.

A structured format helps integration, but form and content are assessed separately. A well-formed identifier can refer to the wrong customer. The application checks existence, access rights and the operation's conditions, including human approval when the process requires it.

In business automation with AI integration, these controls connect a conversation to a workflow. Rules such as “who can make changes” are enforced in software, not only through an instruction message given to the model.

Data processing, retention and permissions must be specified separately. For their design, see our guide to data protection when integrating AI into an application. Connecting a source does not mean every user can see all its content.

Practical example: an internal procedure assistant

Teaching example: an employee asks how to request access to the inventory application. The system identifies them and retrieves the approved procedure. The model can explain where to submit the request, who checks it and what happens next, using information from the document.

If the procedure does not specify an approver for that department, the answer should identify the missing field and refer the request to the configured contact. Company documentation is not supplemented with an invented person. If the user directly asks for access to be granted, the application retains its separate approval workflow.

During verification, compare the answer with the procedure, check its version and test users with different permissions. Add ambiguous questions, contradictory documents and out-of-scope requests. These cases demonstrate how the complete system works, not merely whether it can write a fluent explanation.

This example does not describe a measured customer outcome. It is an analytical pattern for a business software and application development project in which the assistant explains and prepares information while the system retains control of operations.

How do we evaluate an LLM before business integration?

Begin with a defined task and representative examples. For a procedure assistant, check the correctness of steps, use of sources and treatment of missing information. For data extraction, measure correct fields and validations; for classification, examine errors by category.

Romanian-language tests should include diacritics, company terminology, abbreviations and differently phrased requests. Comparisons use the same cases and document versions. A general benchmark score does not replace these operational checks.

In a prototype, group questions by expected behaviour: a direct source-based answer, a clarification request or referral to a responsible person. Record the reference passage and acceptance conditions for each case. This worksheet makes comparisons repeatable when the model, instructions or documents change, and identifies exactly where a correction is needed.

Track time to the first visible response, total duration, consumption and human intervention separately. In a web interface, model requests can be handled server-side while the interface loads on demand. Providing an AI-assisted feature does not require loading the model into every visitor's browser.

Costs cover integration, source preparation, model use, verification and maintenance. Our guide to AI application costs and maintenance separates these components. A justified choice connects the model to a process, acceptance criteria and concrete responsibilities.

Frequently asked questions about LLMs

What does LLM stand for?

LLM stands for Large Language Model. It is an AI component that processes language using relationships learned during training. For a project, the task, data and integration matter, not just the size label.

Is an LLM the same thing as ChatGPT?

No. LLM describes a class of models, while ChatGPT is a conversational product using models and software features. The interface, product memory, sources and connected tools must be distinguished from the language model itself.

Does an LLM automatically have internet access?

Online information access requires search, browsing or another integration enabled within the system. Without that connection, the answer uses parameters and supplied context. Evaluation checks the source actually consulted and the information it supports.

Does the model automatically learn from every conversation?

Using messages in context is not the same process as updating parameters. New training requires a defined workflow. Conversation retention and any subsequent use are established separately through the chosen service's configuration and terms.

How do we test an LLM for Romanian-language use?

Build a Romanian question set containing diacritics, abbreviations and actual company terminology. Check meaning, source use and the output format. Include variations of the same request and cases for which the documentation contains no answer.

What should be established before integrating an LLM?

The assisted process, users, permitted sources, expected output and acceptance criteria. Then define approvals, missing-information handling, logging and the operating budget. These elements turn access to a model into a verifiable business feature.