Large Language Models (LLMS) explained: How they work & why they matter

A Large Language Model (LLM) is a type of artificial intelligence trained on vast amounts of text to understand, generate and respond to human language. It powers many of today's AI applications, from intelligent chatbots and virtual assistants to content generation, coding support and enterprise search.

As AI adoption accelerates across the UK, LLMs are becoming a business capability rather than an emerging technology. According to the Office for National Statistics, 71% of UK organisations were using at least one AI technology, with LLMs among the most widely adopted. Whether your goal is to improve productivity, enhance customer experiences or drive digital transformation, understanding how LLMs work, where they deliver value, and the risks they present is essential.

This guide explains everything you need to know, and how OneAdvanced's IQ brings that capability into UK organisations with the governance layer that public tools typically lack.

See LLM technology at work in your organisation IQ, OneAdvanced's Intelligent System of Work, brings governed, UK-hosted large language model capabilities directly into your everyday workflows. Book a Demo

What is a Large Language Model?

A Large Language Model (LLM) is a type of artificial intelligence trained on vast datasets containing billions of words from books, websites and other sources. It learns patterns in language to understand prompts and generate human-like, contextually relevant responses. This enables them to perform a wide range of business tasks, from answering customer queries and summarising reports to translating languages, writing code and drafting emails.

LLMs are powered by Transformer architecture, which analyses the relationships between all the words in a sentence simultaneously rather than processing them one by one. This allows the model to understand context, nuance and intent far more effectively than earlier natural language processing (NLP) technologies.

At the heart of this architecture is self-attention: a mechanism that identifies which words and phrases are most relevant when generating the next response. Much like a person referring back to earlier chapters of a book to understand the story, an LLM continually considers the broader context before producing an answer. This ability to retain context is what makes these models significantly more capable, accurate and versatile than previous generations of language technology.

The software behind LLMs

Large Language Models rely on sophisticated software to process vast amounts of data, recognise patterns, and generate human-like text. They include these core components that work together to make this possible.

Machine learning (ML)

Machine learning algorithms train LLMs to understand and generate language. Using deep learning techniques, models analyse large volumes of text to identify patterns, relationships, and linguistic structures. Through repeated training and refinement, they continuously improve their accuracy, enabling more relevant and context-aware responses over time.

Transformer model

The transformer is the foundation of modern LLMs. Unlike traditional language models that process words sequentially, transformers analyse relationships between all words in a sentence simultaneously, enabling a deeper understanding of context. Their self-attention mechanism helps identify which words are most relevant to one another, resulting in more accurate, coherent, and contextually appropriate outputs.

Natural Language Processing (NLP)

LLMs use a range of NLP techniques to understand human language. These include tasks such as syntax and semantic analysis, sentiment detection, and entity recognition. Together, these capabilities help models interpret meaning, identify intent and emotion, and recognise people, organisations, locations, and other key information within text.

Neural networks

Neural networks are computational systems inspired by the structure of the human brain. Made up of interconnected layers of nodes, they enable LLMs to process information, identify patterns, and make predictions. By leveraging deep learning, neural networks can learn from massive datasets and continually improve their ability to understand and generate language.

Datasets

Datasets are the foundation of LLM training. They typically contain millions or billions of words collected from sources such as books, articles, websites, forums, and social media. The data quality, diversity, and scale directly influence a model's ability to understand language, capture nuance, and produce accurate, human-like responses.

The hardware behind LLMs

While the software gets most of the attention, an LLM is nothing without the physical infrastructure behind it. Central Processing Units (CPUs) once handled the bulk of enterprise computing, but they can't run the sheer number of simultaneous calculations that LLM training demands. Two specialised chip types have taken over:

• Graphics Processing Units (GPUs): Originally built for rendering images, GPUs process thousands of calculations in parallel, making them well suited to training neural networks on huge datasets.
• Tensor Processing Units (TPUs): Purpose-built by Google for machine learning workloads, TPUs are optimised specifically for the matrix operations LLMs rely on, offering further gains in speed and efficiency.

Memory matters just as much as processing power. Training and running an LLM requires fast, high-bandwidth memory that can feed data to the processor without creating bottlenecks. A mismatch here undermines even the most powerful GPU or TPU cluster.

Cloud computing has become the practical route into this infrastructure for most organisations. Rather than buying and maintaining physical High-Performance Computing (HPC) systems, businesses can access GPU and TPU capacity on demand, scaling up or down as projects require. UK organisations increasingly weigh this against data-sovereignty requirements too, considering not just how much compute a provider offers, but where the underlying data physically sits and how it's governed.

Key hardware & adoption challenges

Training and running LLMs at scale surfaces five recurring challenges:

1. Computational cost: Billions of parameters require HPC-grade GPU or TPU clusters, putting serious in-house model development expensive or out of reach for most organisations.
2. Energy use: AI's power appetite is now a national infrastructure issue. UK data centres, increasingly AI-driven, were estimated to consume around 5.8% of the country's electricity generation in 2026, a figure the UK Government has warned could quadruple by 2030 as demand accelerates.
3. Memory bandwidth and latency: LLMs need to move enormous volumes of data in and out of memory quickly; upgrading memory systems to keep pace is a genuinely expensive undertaking.
4. Scalability: As models grow, so must the compute, memory and cooling behind them — a moving target that strains budgets and timelines alike.
5. Accessibility: Smaller and mid-sized organisations without hyperscale budgets or dedicated AI teams can find themselves priced out of building or fine-tuning their own models — one reason why 46% of small UK firms say they lack the in-house knowledge and skills to use AI at all.

These challenges are precisely why most UK organisations now access LLM capability through a governed platform rather than attempting to build and train models themselves.

How do large language models work?

Here is the step-by-step breakdown of how LLMs work in detail:

Step 1: Collecting information

The journey starts by gathering a vast amount of written data, obtained from various sources like books, articles, and websites. The more extensive and diverse the dataset, the deeper the model's understanding of language and general knowledge.

Step 2: Tokenisation

Tokenisation involves dividing text into smaller, meaningful components. These units could consist of words, phrases, sentences, or even single characters. The objective is to generate a numeric depiction of every word in order to input it into the model for in-depth analysis and understanding of text.

Step 3: Pre-training

In this stage, the LLM gains knowledge from the tokenised text by predicting upcoming tokens using the ones that come before them. This unsupervised learning stage is essential for the model to understand language structure, grammar, and meaning. Pre-training commonly utilises a transformer structure, which relies on the self-attention mechanism to grasp the connections between tokens.

Step 4: Fine-tuning

Following pre-training, LLMs go through a fine-tuning process. Fine-tuning involves adapting a pre-trained model to suit a particular language task. This includes providing the model with extra data related to the particular task, such as sentiment analysis or question-answering. The model tweaks its parameters in order to improve its performance in this particular task.

Step 5: Inference

After fine-tuning, LLMs are prepared for utilisation in different language assignments. During this stage, the model processes fresh input data and employs its acquired knowledge and algorithms to generate coherent and contextually suitable answers. The more data the LLM has been trained on, the better it will perform at the inference stage.

Step 6: Contextual understanding

LLMs excel at understanding context and generating responses that take into consideration the context provided. The self-attention mechanisms of the transformer play a critical role in enabling the model to grasp complex contextual information and long-distance connections.

Step 7: Beam search

Beam search is an additional method employed by LLMs in order to produce numerous responses and select the most suitable one by considering linguistic and contextual hints. This assists the model in generating responses that resemble those of humans more closely, improving both its accuracy and fluency.

Step 8: Generate responses

Once the input data is processed and its context is understood, the LLM creates responses with advanced algorithms and its acquired knowledge. These answers may vary from brief one-word responses to longer sentences or paragraphs, based on the nature of the assignment. It can diverse, creative, imaginative, and extremely relevant, closely imitating human language production.

To summarise, LLMs go through a multi-step process that gives them the ability to understand language complexities, capture contextual subtleties, and generate text that reflects human language creativity and coherence.

Why large language models matter for business

UK adoption of AI has moved from experimentation to genuine momentum. As already highlighted in the introduction of this article, 71% of UK businesses are using at least one AI technology, up from single figures three years earlier, with large language models the single most widely used type of AI in business.

Despite that progress, only around 31% of UK organisations report a positive return on their AI investment so far, even though 75% of those that have adopted AI report productivity gains , which is a major gap between adoption and value realisation that mirrors what OneAdvanced's own Annual Trends Report 2026 found across UK organisations more broadly.

For business leaders, the application of large language models sits in a few concrete areas:

• Content creation: LLM-powered tools can draft articles, product copy and marketing materials at speed while holding a consistent tone and brand voice, freeing teams to focus on strategy and editing rather than first drafts. This is one of the clearest examples of the broader benefits of AI for content-heavy teams.
• Customer service automation: Chatbots and virtual assistants built on LLMs can handle routine enquiries around the clock, understanding intent and sentiment well enough to escalate only the queries that genuinely need a human.
• Translation services: Modern LLM-powered translation considers the context of a whole sentence or passage, not just individual words, producing far more natural results than older rules-based systems.
• Personal and workplace assistants: From scheduling to voice commands, LLMs sit behind much of the AI in the workplace shift already under way in UK organisations, alongside emerging AI agents that can act on multi-step tasks rather than simply answering questions.

In regulated UK sectors, the use cases are still more concrete. In healthcare, for example, OneAdvanced's GP Workflow Assistant (Docman AI) uses LLM and NLP techniques to summarise and code clinical documents, saving GP practice staff time on administrative work without compromising clinical accuracy.

Turn LLM potential into measurable business value See how OneAdvanced's sector-specific AI capabilities across healthcare, legal, education, housing and the public sector help teams put large language models to work safely and productively. Book a Demo

Popular large language models in 2026

The large language model landscape moves fast, and today's leading models will not be the same names organisations are using in another year. As a snapshot of the current field:

Model family	Provider	Best used for	Notable strength
GPT-5 series	OpenAI	General-purpose assistant, broad ecosystem	Widest range of connectors, voice and image generation, strong agentic tool use
Claude (Sonnet / Opus family)	Anthropic	Long-form writing, coding, enterprise reasoning	Large context window and strong performance on complex, multi-step reasoning
Gemini 3 series	Google	Large-scale document and data analysis	Very large context window and native integration across Google Workspace and Search
Sector-embedded LLMs (e.g. within a governed platform)	Enterprise platforms such as OneAdvanced IQ	Task-specific, governed workflows	Combines general LLM capability with an organisation's own data, permissions and compliance controls

Risks and limitations to plan for

No large language model is infallible, and understanding the limits matters as much as understanding the mechanics:

• Hallucination: LLMs can generate plausible-sounding but factually incorrect statements, particularly on niche or recent topics outside their training data. Any output used in a regulated or customer-facing context needs a human review step.
• Data privacy and GDPR: Feeding sensitive customer, employee or patient data into a public LLM can create real compliance exposure. UK data protection law applies in full to AI-processed data.
• Bias: Because LLMs learn from existing text, they can reproduce the biases present in that data, which is a real consideration for anything touching hiring, lending or public services.
• Cost and unpredictability: Usage-based pricing on public LLM APIs can scale unpredictably as adoption spreads across a workforce, without the governance to track who is using what.

These risks are exactly why AI governance has moved from a technical afterthought to a boardroom concern across the UK's more regulated sectors.

How OneAdvanced helps UK organisations use LLMS safely

OneAdvanced's IQ – The Intelligent System of Work brings large language model capability into UK organisations with the governance layer that public tools often lack. IQ is built around ISO 42001-aligned AI governance and UK-hosted data sovereignty, giving organisations in regulated sectors a way to put LLMs to work without their data leaving UK control.

Rather than treating AI as a single chatbot bolted onto existing software, IQ embeds AI agents directly into the flow of work, built on a shared data layer that already understands an organisation's sector-specific context, whether that's healthcare, legal, education, housing or the public sector.

Ready to put large language models to work, safely? Talk to OneAdvanced about how IQ brings governed, UK-hosted AI into your organisation's everyday workflows, built for regulated sectors, from day one. Book a Demo | Contact Us

Frequently Asked Questions (FAQs)

How do large language models actually generate text?

They break input into tokens, predict the most likely next token based on patterns learned during training, and repeat that process, guided by self-attention, to build coherent, contextually relevant responses.

What is the difference between an LLM and generative AI?

Generative AI is the broader category covering any AI that creates new content, such as text, images, audio or video. A large language model is a specific type of generative AI focused on language.

What is the difference between NLP and an LLM?

NLP is the wider field of computers processing and understanding human language. An LLM is a large, Transformer-based model that represents today's most advanced application of NLP techniques.

Is it safe to use large language models with sensitive or regulated business data?

Only with the right controls in place. Public consumer LLM tools generally aren't designed for regulated data; organisations in healthcare, legal, government or financial services need a governed platform with clear data residency, access controls and audit trails.

How can UK organisations start using LLMs responsibly?

Start with a clear use case, involve compliance and data teams early, choose a platform with UK data hosting and recognised AI governance standards (such as ISO 42001), and train staff before scaling. The skills gap, not the technology itself, is the biggest barrier for most UK organisations today.