What are Large Language Models?
15 min read
Large Language Models (LLMs) are AI systems trained on vast amounts of text. They learn patterns in how language works by processing trillions of words from books, articles, websites, and conversations. This training enables them to generate human-like responses to your questions and prompts.
How do LLMs learn?
Large Language Models are trained on an unprecedented amount of textual data. Everything from books and academic articles to websites, code repositories, and social media conversations.
To give a sense of scale, these systems are trained on datasets that can contain trillions of words. The equivalent of reading the entire contents of a vast library hundreds of times over.
However, "trained" doesn't mean what you might think. LLMs don't study this content like a student might. Instead, this extraordinary volume of data enables them to identify patterns. They learn linguistic structures and contextual nuances across diverse domains.
LLMs learn how words tend to follow one another. How ideas connect. How language flows in different contexts.
Think of it this way. If you've read thousands of fairy tales, you develop an intuition for how they work. When you see the phrase "Once upon a time," you instinctively anticipate what might come next. LLMs operate in a similar way. But on a massive scale and across all types of text, not just fairy tales.
How do LLMs process information?
LLMs can hold sophisticated conversations on virtually any topic. But their knowledge is based on pattern recognition, not real-world understanding.
They can discuss gravity in detail. But this understanding comes entirely from text. Scientific explanations and analogies written by others. They've never experienced falling or weight.
Similarly, they can describe how an apple tastes. Sweet, crisp, tart. But only because these words appear together frequently in their training data. They've never actually tasted anything.
This fundamental limitation means LLMs work from associations in text, not from lived experience. Their "understanding" is a simulation built from language patterns.
How do LLMs handle nuanced or ambiguous language?
Humans interpret meaning from subtle cues, sarcasm, or incomplete statements. The phrase "I'm fine" can mean very different things depending on tone or context.
LLMs derive meaning purely from text patterns. If an LLM encounters "I'm fine" in a positive context (e.g., "She smiled and said, 'I'm fine.'"), it may interpret it as genuine. In a negative context (e.g., "He sighed and muttered, 'I'm fine.'"), it might miss the sarcasm unless these patterns are explicitly linked in its training data.
This reliance on explicit patterns makes LLMs less adept at handling layered communication.
Language is deeply tied to culture, context, and shared experiences. While LLMs can process multiple languages, they often lack the cultural awareness that gives words their full meaning.
An LLM might accurately translate the literal meaning of "spill the beans." But without cultural context, it might not recognise this means "reveal a secret."
Similarly, when processing text about cultural rituals or social norms, the LLM might miss the significance that only comes from lived cultural experience.
How do training data limitations affect outputs?
Think of an LLM as a student whose knowledge comes solely from the books it's read. What's in those books, and what's missing, shape what it can understand and discuss.
If a dataset includes numerous texts about Western history but very few about African history, the model's responses will reflect this imbalance. It will offer more detailed insights on the former than the latter.
Additionally, most LLMs have a knowledge cutoff date. They don't know about events, policy changes, or developments after their training data was finalised. An LLM trained before 2023 won't know about groundbreaking renewable energy developments or major geopolitical events from 2024 or 2025.
This makes LLMs useful for foundational knowledge but less reliable for current information or underrepresented topics.
What are LLMs good at?
Understanding what LLMs do well helps you identify where they can genuinely support your work.
LLMs can help you create first drafts or refine existing content. They're useful for turning rough notes into structured documents, adjusting tone for different audiences, or improving clarity. You might use them to draft a response to a consultation, create an internal briefing, or restructure a report. The key is treating the output as a starting point that you review and refine.
They can also condense lengthy reports, consultation responses, or policy documents into key themes and findings. This saves time when you need to extract main points from substantial material. You might summarise a 50-page consultation response into the main arguments and concerns raised, or distill multiple stakeholder submissions to identify common themes. However, summaries inevitably lose detail. Important nuances, caveats, or minority views may be omitted. Critical context that affects interpretation can disappear. Always review summaries against source material when they'll inform decisions or public-facing outputs.
LLMs can take technical or specialist language and express it in simpler terms. This is useful when you need to make complex policy, regulation, or technical information accessible to a general audience. You might ask it to explain a technical regulation in plain English for public guidance, or to rephrase specialist terminology for non-expert stakeholders. But be cautious, especially with legal or specialised content. Simplification can change meaning. A nuance that seems minor might be legally significant. Always verify that the simplified version accurately preserves the meaning and intent of the original.
When you're developing proposals or exploring approaches, LLMs can suggest alternatives or variations. They can help you think through different ways to structure an argument, frame a policy, or approach a problem. This works best when you have a clear starting point and want to explore variations, not when you need expert judgment on which approach is best.
LLMs also excel at taking content in one format and presenting it differently. Converting meeting notes into action points. Turning a document into an FAQ. Reformatting data into tables or summaries. This is essentially pattern recognition applied to structure rather than meaning.
What are LLMs not so good at?
Understanding limitations is as important as understanding capabilities. LLMs fall short in several critical areas.
LLMs don't verify facts. They generate plausible-sounding responses based on patterns in their training data. This means they can confidently state incorrect information. When you ask "What is the capital of France?" the LLM recognises the strong pattern between "Paris" and "capital of France" in its training data. But it uses the same pattern-matching approach for questions where the "right" answer isn't as clear-cut. This makes them unreliable for statistics, dates, legal requirements, current events, or any factual claims that matter to your work.
LLMs can explain what regulations say, but they cannot provide authoritative legal interpretation or advice on how regulations apply to specific circumstances. If you need to understand legal obligations, compliance requirements, or how legislation applies, consult appropriate legal or specialist expertise.
Recent models have shown impressive improvements in mathematical problem-solving. Some achieve high scores on competition-level mathematics. However, research shows this isn't genuine mathematical reasoning. When numerical values change or irrelevant information is added to problems, performance drops dramatically. LLMs use sophisticated pattern matching rather than logical deduction.
This means they can appear to solve mathematical problems correctly but aren't reliably performing the logical steps needed. They're recognising patterns similar to problems in their training data. Never rely on LLMs for numerical analysis, financial calculations, or statistical work without independent verification. Even when answers look correct, the reasoning may be flawed.
Most LLMs have a knowledge cutoff date. They don't know about events, policy changes, or developments that happened after their training data was finalised. Some systems can search the web to supplement their knowledge. But even then, verify important current information against authoritative sources.
LLMs generate outputs based on patterns. They have no accountability for those outputs. You do. They can help inform your thinking. But the decision, and responsibility for it, remains with you.
What does this mean for you?
LLMs are powerful pattern-matching systems that can generate remarkably human-like text. This makes them useful for many tasks. It also makes their limitations easy to overlook.
The key is matching the tool to the task. Use LLMs where pattern matching and language generation add value. But maintain human oversight for anything that requires accuracy, accountability, or expert judgment.
When you understand what LLMs actually do, rather than what they appear to do, you can make better decisions about when and how to use them.