7 Concepts Behind Large Language Models Explained in 7 Minutes

If you’ve been using large language models like GPT-4 or Claude, you’ve probably wondered how they can write actually usable code, explain complex topics, or even help you debug your morning coffee routine (just kidding!).

But what’s actually happening under the hood? How do these systems transform a simple prompt into coherent, contextual responses that sometimes feel almost human?

Today, we’re going to learn more about the core concepts that make large language models work. Whether you’re a developer integrating LLMs into your applications, a product manager trying to understand capabilities and limitations, or simply someone curious, this article is for you.

1. Tokenization

Before any text reaches a neural network, it must be converted into numerical representations. Tokenization is this translation process, and it’s more sophisticated than simply splitting on whitespace or punctuation.

Tokenizers use algorithms like Byte Pair Encoding (BPE), WordPiece, or SentencePiece to create vocabularies that balance efficiency with representation quality.

These algorithms construct subword vocabularies by beginning with individual characters and progressively combining the most commonly occurring pairs. For example, “unhappiness” might be tokenized as [“un”, “happy”, “ness”], allowing the model to understand the prefix, root, and suffix separately.

This subword approach solves many essential problems. It handles out-of-vocabulary words by breaking them into known pieces. It manages morphologically rich languages where words have many variations. Most importantly, it creates a fixed vocabulary size that the model can work with typically 32K to 100K tokens for modern LLMs.

The tokenization approach determines both model efficiency and computational expenses. Effective tokenization shortens sequence lengths, thereby reducing processing demands.

GPT-4’s 8K context window allows for 8,000 tokens, approximately equivalent to 6,000 words. When you’re building applications that process long documents, token counting becomes crucial for managing costs and staying within limits.

2. Embeddings

You’ve probably seen articles or social media posts on embeddings and popular embedding models. But what are they, really? Embeddings transform discrete tokens into vector representations, typically in hundreds or thousands of dimensions.

Here’s where things get interesting. Embeddings are dense vector representations that also capture semantic meaning. Instead of treating words as arbitrary symbols, embeddings place them in a multi-dimensional space where similar concepts cluster together.

Picture a map where “king” and “queen” are close neighbors, but “king” and “bicycle” are continents apart. That’s essentially what embedding space looks like, except it’s happening across hundreds or thousands of dimensions simultaneously.

When you’re building search functionality or recommendation systems, embeddings are your secret weapon. Two pieces of text with similar embeddings are semantically related, even if they don’t share exact words. This is why modern search can understand that “automobile” and “car” are essentially the same thing.

3. The Transformer Architecture

The transformer architecture revolutionized natural language processing (yes, literally!) introducing attention. Instead of processing text sequentially like older models, transformers can look at all parts of a sentence simultaneously and figure out which words are most important for understanding each other word.

When processing “The cat sat on the mat because it was comfortable,” the attention mechanism helps the model understand that “it” refers to “the mat,” not “the cat.” This happens through learned attention weights that strengthen connections between related words.

For developers, this translates to models that can handle long-range dependencies and complex relationships within text. It’s why modern LLMs can maintain coherent conversations across multiple paragraphs and understand context that spans entire documents.

4. Training Phases: Pre-training vs Fine-tuning

LLM development happens in distinct phases, each serving a different purpose. Language models learn patterns from massive datasets through pre-training — an expensive, computationally intensive phase. Think of it as teaching a model to understand and generate human language in general.

Fine-tuning comes next, where you specialize pre-trained models for your specific tasks or domains. Instead of learning language from scratch, you’re teaching an already-capable model to excel at particular applications like code generation, medical diagnosis, or customer support.

Why is this approach efficient? You don’t need a ton of resources to create powerful, specialized models. Companies are building domain-specific LLMs by fine-tuning existing models with their own data, achieving impressive results with relatively modest computational budgets.

5. Context Windows

Every LLM has a context window — the maximum amount of text it can consider at once. You can conceptualize it as the model’s operational memory. Everything beyond this window simply doesn’t exist from the model’s perspective.

This can be quite challenging for developers. How do you build a chatbot that remembers conversations across multiple sessions when the model itself has no persistent memory? How do you process documents longer than the context window?

Some developers maintain conversation summaries, feeding them back to the model to maintain context. But that’s just one of the ways to do it. Here are a few more possible solutions: using memory in LLM systems, retrieval-augmented generation (RAG), and sliding window techniques.

6. Temperature and Sampling

Temperature helps balance the randomness versus predictability in a language model’s generated responses. At temperature 0, the model always picks the most probable next token, producing consistent but potentially repetitive results. Higher temperatures introduce randomness, making outputs possibly more creative but less predictable.

Essentially, temperature determines the probability distribution over the model’s vocabulary. At low temperatures, the model strongly favors high-probability tokens. At high temperatures, it gives lower-probability tokens a better chance of being selected.

Sampling techniques such as top-k and nucleus sampling provide additional control mechanisms for text generation. Top-k sampling restricts the selection to the k highest-probability tokens, whereas nucleus sampling adaptively determines the candidate set by using cumulative probability thresholds.

These techniques help balance creativity and coherence, giving developers more fine-grained control over model behavior.

7. Model Parameters and Scale

Model parameters are the learned weights that encode everything an LLM knows.  Most large language models typically have hundreds of billions of parameters, while larger models push into the trillions. These parameters capture patterns in language, from basic grammar to complex reasoning abilities.

More parameters generally mean better performance, but the relationship isn’t linear. Scaling up model size demands exponentially greater computational resources, datasets, and training duration.

For practical development, parameter count affects inference costs, latency, and memory requirements. A 7-billion parameter model might run on consumer hardware, while a 70-billion parameter model needs enterprise GPUs. Understanding this trade-off helps developers choose the right model size for their specific use case and infrastructure constraints.

Wrapping Up

The concepts we’ve covered in this article form the technical core of every LLM system. So what’s next?

Go build something that helps you understand language models better. Also try to do some reading on the go. Start with the seminal papers like the “Attention Is All You Need” paper, explore embedding techniques, and experiment with different tokenization strategies on your own data.

Set up a local model and watch how temperature changes affect outputs. Profile memory usage across different parameter sizes. Happy experimenting!