Understanding Large Language Models
The AI systems reshaping how we communicate, create, and solve problems — explained from first principles.
What is a Large Language Model?
A Large Language Model (LLM) is a type of artificial intelligence trained on vast amounts of text to understand and generate human language. Think of it as a very sophisticated pattern-recognition system — one that has read billions of documents, books, and web pages, and learned the statistical relationships between words, phrases, and ideas.
Unlike a search engine that retrieves stored information, an LLM generates new text by predicting what comes next, word by word, based on everything it has learned. The result is a system that can converse, explain, summarize, translate, code, and reason in natural language.
Tokens: The Language of LLMs
LLMs don’t read text letter by letter or word by word — they use tokens, which are chunks of characters. The sentence “The cat sat.” might become four tokens: The, cat, sat, .
Here’s how a sentence gets tokenized:
How Training Works
Training an LLM happens in stages:
Pre-training
The model reads trillions of tokens from the internet, books, and code. It learns to predict the next token, over and over, adjusting billions of internal parameters (weights) until its predictions improve.
Fine-tuning & Instruction Tuning
The raw model is further trained on curated examples to follow instructions, answer questions helpfully, and behave safely. This shapes a general predictor into a useful assistant.
Reinforcement Learning from Human Feedback (RLHF)
Human raters compare model outputs and express preferences. A reward model is trained on these preferences, and the LLM is optimised to produce responses humans rate as helpful, harmless, and honest.
Transformer Architecture
Almost all modern LLMs are built on the Transformer, introduced in 2017. It uses self-attention — a mechanism that lets every token consider every other token in context — to capture long-range relationships in text.
Parameters = Learned Knowledge
A model’s parameters (weights) encode everything it learned during training. GPT-3 has 175 billion parameters; modern frontier models may have trillions. More parameters can mean more capacity — but also more compute cost.
Context Window
The context window is how much text the model can “see” at once — its working memory. Early models had ~4 K tokens; today’s models support 100 K–1 M+ tokens, enabling whole-book comprehension.
Temperature & Sampling
Temperature controls randomness. Low temperature → focused, predictable outputs. High temperature → creative, diverse, sometimes surprising ones. Most assistants run at a moderate setting by default.
Real-World Applications
LLMs are general-purpose — the same underlying model can power wildly different applications:
Limitations to Know
🌀 Hallucinations
LLMs can confidently generate plausible-sounding but factually incorrect information. Always verify important claims from authoritative sources.
📅 Knowledge Cutoff
Training data has a cutoff date. Models don’t know about events that happened after they were trained unless given external tools or updated context.
🪞 No True Understanding
LLMs are pattern matchers, not reasoners in the human sense. They can fail at novel logical puzzles and lack genuine beliefs or experiences.
⚖️ Bias
Models inherit biases from training data. Outputs may reflect historical prejudices or amplify stereotypes present in text scraped from the web.
The Scale of Modern LLMs
To appreciate what “large” means, consider these rough figures for frontier models:
Looking Forward
LLMs are evolving rapidly. Current frontiers include multimodal models that see images and listen to audio, tool-using agents that browse the web and run code, long-context models that can reason over entire codebases, and ongoing research into interpretability — understanding why a model produces a given output.
Whether you’re a curious learner, a developer, or a decision-maker, understanding LLMs gives you a clearer view of one of the most transformative technologies of our time.