attention mechanism

How do LLMs work? It’s a question that sits at the heart of modern AI innovation. From writing assistants and chatbots to code generators and search engines, large language models (LLMs) are transforming the way machines interact with human language. Every time you type a prompt into ChatGPT or any other LLM-based tool, you’re initiating a complex pipeline of mathematical and neural processes that unfold within milliseconds.

In this post, we’ll break down exactly how LLMs work, exploring every critical stage, tokenization, embedding, transformer architecture, attention mechanisms, inference, and output generation. Whether you’re an AI engineer, data scientist, or tech-savvy reader, this guide is your comprehensive roadmap to the inner workings of LLMs.

What Is a Large Language Model?

A large language model (LLM) is a deep neural network trained on vast amounts of text data to understand and generate human-like language. These models are the engine behind AI applications such as ChatGPT, Claude, LLaMA, and Gemini. But to truly grasp how LLMs work, you need to understand the architecture that powers them: the transformer model.

Key Characteristics of LLMs:

• Built on transformer architecture
• Trained on large corpora using self-supervised learning
• Capable of understanding context, semantics, grammar, and even logic
• Scalable and general-purpose, making them adaptable across tasks and industries

Learn more about LLMs and their applications.

Why It’s Important to Understand How LLMs Work

LLMs are no longer just research experiments, they’re tools being deployed in real-world settings across finance, healthcare, customer service, education, and software development. Knowing how LLMs work helps you:

• Design better prompts
• Choose the right models for your use case
• Understand their limitations
• Mitigate risks like hallucinations or bias
• Fine-tune or integrate LLMs more effectively into your workflow

Now, let’s explore the full pipeline of how LLMs work, from input to output.

7 Best Large Language Models (LLMs) You Must Know About

Step-by-Step: How Do LLMs Work?

Step 1: Tokenization – How do LLMs work at the input stage?

The first step in how LLMs work is tokenization. This is the process of breaking raw input text into smaller units called tokens. Tokens may represent entire words, parts of words (subwords), or even individual characters.

Tokenization serves two purposes:

1. It standardizes inputs for the model.
2. It allows the model to operate on a manageable vocabulary size.

Different models use different tokenization schemes (Byte Pair Encoding, SentencePiece, etc.), and understanding them is key to understanding how LLMs work effectively on multilingual and domain-specific text.

Step 2: Embedding – How do LLMs work with tokens?

Once the input is tokenized, each token is mapped to a high-dimensional vector through an embedding layer. These embeddings capture the semantic and syntactic meaning of the token in a numerical format that neural networks can process.

However, since transformers (the architecture behind LLMs) don’t have any inherent understanding of sequence or order, positional encodings are added to each token embedding. These encodings inject information about the position of each token in the sequence, allowing the model to differentiate between “the cat sat on the mat” and “the mat sat on the cat.”

This combined representation—token embedding + positional encoding—is what the model uses to begin making sense of language structure and meaning. During training, the model learns to adjust these embeddings so that semantically related tokens (like “king” and “queen”) end up with similar vector representations, while unrelated tokens remain distant in the embedding space.

Step 3: Transformer Architecture – How do LLMs work internally?

At the heart of how LLMs work is the transformer architecture, introduced in the 2017 paper “Attention Is All You Need.” The transformer is a sequence-to-sequence model that processes entire input sequences in parallel—unlike RNNs, which work sequentially.

Key Components:

• Multi-head self-attention: Enables the model to focus on relevant parts of the input.
• Feedforward neural networks: Process attention outputs into meaningful transformations.
• Layer normalization and residual connections: Improve training stability and gradient flow.

The transformer’s layered structure, often with dozens or hundreds of layers—is one of the reasons LLMs can model complex patterns and long-range dependencies in text.

Step 4: Attention Mechanisms – How do LLMs work to understand context?

If you want to understand how LLMs work, you must understand attention mechanisms.

Attention allows the model to determine how much focus to place on each token in the sequence, relative to others. In self-attention, each token looks at all other tokens to decide what to pay attention to.

For example, in the sentence “The cat sat on the mat because it was tired,” the word “it” likely refers to “cat.” Attention mechanisms help the model resolve this ambiguity.

Types of Attention in LLMs:

• Self-attention: Token-to-token relationships within a single sequence.
• Cross-attention (in encoder-decoder models): Linking input and output sequences.
• Multi-head attention: Several attention layers run in parallel to capture multiple relationships.

Attention is arguably the most critical component in how LLMs work, enabling them to capture complex, hierarchical meaning in language.

 

LLM Finance: The Impact of Large Language Models in Finance

Step 5: Inference – How do LLMs work during prediction?

During inference, the model applies the patterns it learned during training to generate predictions. This is the decision-making phase of how LLMs work.

Here’s how inference unfolds:

1. The model takes the embedded input sequence and processes it through all transformer layers.

2. At each step, it outputs a probability distribution over the vocabulary.

3. The most likely token is selected using a decoding strategy:

   • Greedy search (pick the top token)

   • Top-k sampling (pick from top-k tokens)

   • Nucleus sampling (top-p)

4. The selected token is fed back into the model to predict the next one.

This token-by-token generation continues until an end-of-sequence token or maximum length is reached.

Step 6: Output Generation – From Vectors Back to Text

Once the model has predicted the entire token sequence, the final step in how LLMs work is detokenization—converting tokens back into human-readable text.

Output generation can be fine-tuned through temperature and top-p values, which control randomness and creativity. Lower temperature values make outputs more deterministic; higher values increase diversity.

How to Tune LLM Parameters for Optimal Performance

Prompt Engineering: A Critical Factor in How LLMs Work

Knowing how LLMs work is incomplete without discussing prompt engineering—the practice of crafting input prompts that guide the model toward better outputs.

Because LLMs are highly context-dependent, the structure, tone, and even punctuation of your prompt can significantly influence results.

Effective Prompting Techniques:

1. Use examples (few-shot or zero-shot learning)
2. Give explicit instructions
3. Set role-based context (“You are a legal expert…”)
4. Add delimiters to structure content clearly

Mastering prompt engineering is a powerful way to control how LLMs work for your specific use case.

Learn more about prompt engineering strategies.

How Do LLMs Work Across Modalities?

While LLMs started in text, the principles of how LLMs work are now being applied across other data types—images, audio, video, and even robotic actions.

Examples:

• Code generation: GitHub Copilot uses LLMs to autocomplete code.
• Vision-language models: Combine image inputs with text outputs (e.g., GPT-4V).
• Tool-using agents: Agentic AI systems use LLMs to decide when to call tools like search engines or APIs.

Understanding how LLMs work across modalities allows us to envision their role in fully autonomous systems.

Explore top LLM use cases across industries.

Summary Table: How Do LLMs Work?

Frequently Asked Questions

Q1: How do LLMs work differently from traditional NLP models?

Traditional models like RNNs process inputs sequentially, which limits their ability to retain long-range context. LLMs use transformers and attention to process sequences in parallel, greatly improving performance.

Q2: How do embeddings contribute to how LLMs work?

Embeddings turn tokens into mathematical vectors, enabling the model to recognize semantic relationships and perform operations like similarity comparisons or analogy reasoning.

Q3: How do LLMs work to generate long responses?

They generate one token at a time, feeding each predicted token back as input, continuing until a stopping condition is met.

Q4: Can LLMs be fine-tuned?

Yes. Developers can fine-tune pretrained LLMs on specific datasets to specialize them for tasks like legal document analysis, customer support, or financial forecasting. Learn more in Fine-Tuning LLMs 101

Q5: What are the limitations of how LLMs work?

LLMs may hallucinate facts, lack true reasoning, and can be sensitive to prompt structure. Their outputs reflect patterns in training data, not grounded understanding. Learn more in Cracks in the Facade: Flaws of LLMs in Human-Computer Interactions

Conclusion: Why You Should Understand How LLMs Work

Understanding how LLMs work helps you unlock their full potential, from building smarter AI systems to designing better prompts. Each stage—tokenization, embedding, attention, inference, and output generation—plays a unique role in shaping the model’s behavior.

Whether you’re just getting started with AI or deploying LLMs in production, knowing how LLMs work equips you to innovate responsibly and effectively.

Ready to dive deeper?

• Explore Data Science Dojo’s LLM Bootcamp for hands-on learning.
• Read more about transformer models.
• Master prompt engineering for better AI results.

Transformers have transformed Natural Language Processing (NLP) by driving advancements in machine translation and text generation. Introduced in the 2017 paper “Attention Is All You Need,” this architecture replaced traditional recurrent models with self-attention mechanisms, boosting efficiency and performance.

Why are Transformers so effective? How do they achieve accuracy in language processing? This blog will explore their components—self-attention, multi-head attention, and positional encoding—to understand their role in today’s language models.

Transformers have revolutionized natural language processing with their use of self-attention mechanisms. In this blog, we will study the key components of transformers to understand how they have become the basis of the state of the art in different tasks.  

 

 

Introduction: Attention Is All You Need

The Transformer architecture was first introduced in the 2017 paper “Attention is All You Need” by researchers at Google. Unlike previous sequence models such as RNNs, Transformer relies entirely on self-attention to model dependencies in sequential data like text.

Large Language Models Knowledge Test

 

Remarkably, this simple change led to major improvements in machine translation quality over existing methods. Since then, Transformers have been applied successfully to diverse NLP tasks like text generation, summarization, and question-answering. Their versatility has even led to applications in computer vision. 

But what exactly is self-attention and why is it so effective? Let’s explore this. 

The Limitations of Recurrent Neural Networks – RNNs

Recurrent neural networks (RNNs) used to be the dominant approach for modeling sequences. An RNN processes textual data incrementally, maintaining a “memory” of the previous context. For example, to predict the next word in a sentence, an RNN model would incorporate information about all the preceding words.  

However, RNNs have certain limitations. They process data sequentially, making parallelization difficult. More critically, they struggle to learn long-range dependencies because the information gets diluted over many steps of time. Attention mechanisms were proposed to mitigate this issue. 

Why Use a Transformer Model?

The transformer architecture has enabled the development of new models that can be trained on large datasets and significantly outperform recurrent neural networks like LSTMs. These new models are utilized for tasks like sequence classification, question answering, language modeling, named entity recognition, summarization, and translation.  

Let’s examine the key components of transformers to understand how they have become the foundation for state-of-the-art performance on different NLP tasks.  

 

Learn how to expand your knowledge with R programming books to upskill NLP

 

Transformer design

A transformer consists of an encoder and a decoder. The encoder’s role is to encode the inputs (i.e. sentences) into a state, often containing multiple tensors. This state is then passed to the decoder to generate the outputs.

In machine translation, the encoder converts a source sentence, e.g. “Hello world“, into a state, such as a vector, that captures its semantic meaning.

The decoder then utilizes this state to produce the translated target sentence, e.g. “Bonjour le monde.” Both the encoder and decoder primarily employ Multi-Head Attention and Feedforward Networks, which are the focus of this article.   

 

Key Transformer Components

1. Input embedding

Embedding aims to create a vector representation of words where words with similar meanings will be close in terms of Euclidean distance. For instance, the words “bathroom” and “shower” are related to the same concept, so their word vectors are close in Euclidean space as they convey similar meanings.  

For the encoder, the authors opted for an embedding size of 512 (i.e. each word is represented by a 512-dimensional vector).  

  

 

2. Positional encoding

The position of a word plays a crucial role in understanding the sequence we want to model.  

Therefore, we add positional information about the word’s location in the sequence to its vector. The authors used the following sinusoidal. 

 

  

 

We will explain positional encoding in more detail with an example.  

 

  

We note the position of each word in the sequence.  

We define dmodel = 512, which represents the size of the embedding vector of each word (i.e. the vector dimension). We can now rewrite the two positional encoding equations as:  

 

 

We can see that the wavelength (i.e. frequency) λt decreases as the dimension increases, this forms a progression along the wave from 2pi to 10000.2pi.  

  

 

 

In this model, the absolute positional information of a word in a sequence is added directly to its initial vector. For this, the positional encoding must have the same size dmodel as the initial word vector.

  

3. Attention mechanism

• Scaled Dot-Product Attention  

 

  

Let’s explain the attention mechanism. The key goal of attention is to estimate the relative relevance of the keywords compared to the query word for the same entity. For this, the attention mechanism takes a query vector Q representing a word, the keys K comprising all other words in the sentence, and values V representing the word vectors.  

In our case, V = Q (for the two self-attention layers). In other words, the attention mechanism provides the significance of a word in a given sentence.  

 

  

When we compute the normalized dot product between the query and the keys, we get a tensor that represents the relative importance of each other word for the query. To go deeper into mathematics, we can try to understand why the authors used a dot product to calculate the relation between two words.  

A word is represented by a vector in Euclidian space, in this case, a vector of size 512. When computing the dot product between Q and KT, we calculate the product between Q’s orthogonal projection onto K. In other words, we estimate the alignment between the query and keyword vectors, returning a weight for each word in the sentence.  

 

 

We then normalize by dk to counteract large Q and K magnitudes which can push the softmax function into regions with tiny gradients. The softmax function regularizes the terms and rescales them between 0 and 1 (i.e., converts the dot product to a probability distribution), with the goal of normalizing all weights between 0 and 1.  

 

 

 

Finally, we multiply the weights (i.e., importance) by the values V to reduce irrelevant words and focus on the most significant words.  

    

 

 

• Multi-Head Attention  

 

 

The key idea is that attention is applied multiple times in parallel on different projections of the input queries, keys, and values. This allows the model to learn different types of dependencies between the input words.  

The input queries (Q), keys (K), and values (V) are each linearly projected h times into smaller subspaces. For example, h=8 times into 64-dimensional spaces.  

Attention is then applied in each of these h projected subspaces in parallel, yielding h different attention outputs.  

 

 

These h outputs are concatenated and linearly projected again to get the final values. The projections allow the model to focus on different positional and semantic relationships between words since each projected subspace captures different information.  

Doing this in parallel (multi-head) instead of sequentially improves efficiency.  

The projection matrices are learned during training to discover the most useful projections. So, in summary, multi-head attention applies the attention mechanism in multiple parallel subspaces to learn different types of dependencies between words in an efficient way.  Let’s dive into the mechanics of encoder-decoder architecture.  

 

  

 

In this section, we’ll explain how the encoder and decoder work together to translate an English sentence into a French one, step by step.  

1. Encoder

• Convert a sequence of tokens to a sequence of vectors by using embeddings.    

 

 

• Add position information in each word vector.  

The key advantage of recurrent neural networks is their knack for understanding relationships between sequences and remembering information. On the other hand, Transformers employ positional encoding to factor in where words are in a sequence.  

• Apply Multi-Head Attention  

  

• Use Feed Forward Network  

 

2. Decoder

• Utilize embeddings to transform a French sentence into vectors.   

 

 

• Add positional details within each word vector.    

  

• Apply Multi-Head Attention  

 

 

• Apply Feed Forward Network  

 

• Apply Multi-Head Attention to the encoder output.  

 

We can observe that the Transformer combines the encoder’s output with the decoder’s input. This enables it to discern the relationship between the vectors that encode the English and French sentences.  

• Apply the Feed Forward Network again.  
• Compute the probability for the next word by using linear + SoftMax block. The decoder returns the highest probability as the next word at the output.  

 

In our case, the next word after “Je” is “suis”.  

 

Final Thoughts

The transformer model outperforms all the models on different benchmarks also there was no difference seen between the translation provided by the algorithm and by humans.   

 

 

Transformers are a major advance in NLP, they exceed RNN by having a lower training cost allowing to train models on larger corpora. Even today, transformers remain the basis of state-of-the-art models such as BERT, Roberta, XLNET, and GPT.  

References: 

https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf 

https://github.com/hkproj/transformer-from-scratch-notes 

http://jalammar.github.io/illustrated-transformer/