Understanding Large Language Models (Simple Explanation)

Introduction: The Brains Behind the Bots

When you chat with ChatGPT, ask Claude a question, or use Google's Gemini, you are interacting with a Large Language Model (LLM). But what exactly is an LLM? How does it understand your questions and generate human-like responses?

In this guide, I will explain Large Language Models in simple terms—no computer science degree required.

What is a Large Language Model?

A Large Language Model is an AI system trained on vast amounts of text data to understand and generate human language. The "large" refers to both the massive amount of training data and the enormous number of parameters (variables) the model uses to process language.

Think of an LLM as a incredibly well-read assistant who has read billions of documents and learned patterns in how humans use language.

How Do LLMs Work? (The Simple Version)

Step 1: Training on Massive Text

LLMs are trained on enormous datasets containing text from books, websites, articles, and more. GPT-4, for example, was trained on hundreds of billions of words.

During training, the model learns:

• Grammar and syntax rules
• Facts and knowledge (with a knowledge cutoff)
• Context and meaning
• Conversation patterns
• Reasoning and logic structures

Step 2: Learning Patterns

The model does not memorise text—it learns patterns. It understands that "the cat sat on the..." is likely followed by "mat" or similar words. It learns relationships between concepts: that "Paris" relates to "France" and "capital city."

Step 3: Generating Responses

When you ask a question, the LLM predicts what words should come next, one at a time. It considers:

• Your question (the prompt)
• The conversation history
• Patterns learned during training
• Probability of each possible next word

This prediction happens incredibly fast, generating coherent paragraphs word by word.

Key Concepts Explained

Parameters

Parameters are the internal variables the model adjusts during training. GPT-4 has hundreds of billions of parameters. More parameters generally mean better understanding and generation, though bigger is not always better.

Tokens

LLMs process text in chunks called tokens—roughly 3-4 characters each. "ChatGPT" might be 2-3 tokens. The model has limits on how many tokens it can process at once (context window).

Context Window

This is how much text the model can "remember" in a conversation. Newer models have larger context windows—GPT-4 can handle about 25,000 words of context.

Fine-Tuning

After initial training, models can be fine-tuned on specific data to improve performance for particular tasks or to align with specific values and behaviours.

What LLMs Can and Cannot Do

What They Do Well

• Generate human-like text
• Answer questions based on training data
• Summarise and translate text
• Help with writing and editing
• Explain concepts in different ways
• Assist with coding and analysis

Limitations

• No Real Understanding: They recognise patterns, not true comprehension
• Hallucinations: Can generate false information confidently
• Knowledge Cutoff: Do not know about recent events
• No Common Sense: May give logically inconsistent answers
• Bias: May reflect biases in training data

Major LLMs You Should Know

GPT-4 (OpenAI)

Powers ChatGPT Plus. Known for strong reasoning, creativity, and broad knowledge.

Claude (Anthropic)

Excels at long documents and nuanced conversations. Strong safety features.

Gemini (Google)

Strong integration with Google services. Good at multimodal tasks (text, images, code).

Llama (Meta)

Open-source model that can be run locally. Popular for custom applications.

The Future of LLMs

Expect to see:

• Larger context windows for longer conversations
• Better reasoning and problem-solving abilities
• Multimodal capabilities (understanding images, audio, video)
• More efficient models that run on smaller devices
• Improved accuracy and reduced hallucinations

Conclusion

Large Language Models are powerful tools that have made sophisticated AI accessible to everyone. Understanding how they work—even at a basic level—helps you use them more effectively and recognise their limitations.

Remember: LLMs are tools, not oracles. Use them wisely, verify important information, and always apply your critical thinking.

For businesses looking to leverage LLMs for automation and customer service, ZappingAI offers solutions powered by the latest language models, tailored for UK business needs.