transformer architecture

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.

Natural language processing (NLP) and large language models (LLMs) have been revolutionized with the introduction of transformer models. These refer to a type of neural network architecture that excels at tasks involving sequences.

While we have talked about the details of a typical transformer architecture, in this blog we will explore the different types of the models.

How to Categorize Transformer Models?

Transformers ensure the efficiency of LLMs in processing information. Their role is critical to ensure improved accuracy, faster training on data, and wider applicability. Hence, it is important to understand the different model types available to choose the right one for your needs.

However, before we delve into the many types of transformer models, it is important to understand the basis of their classification.

Classification by Transformer Architecture

The most fundamental categorization of transformer models is done based on their architecture. The variations are designed to perform specific tasks or cater to the limitations of the base architecture. The very common model types under this category include:

• encoder-only
• decoder-only
• encoder-decoder transformers

Categorization Based on Pre-Training Approaches

While architecture is a basic component of consideration, the training techniques are equally crucial components for transformers. Pre-training approaches refer to the techniques used to train a transformer on a general dataset before finetuning it to perform specific tasks.

Some common approaches that define classification under this category include:

• Masked Language Models (MLMs)
• autoregressive models
• conditional transformers

This presents a general outlook on classifying transformer models. While we now know the types present under the broader categories, let’s dig deeper into each transformer model type.

Read in detail about transformer architectures

Architecture-Based Classification

1. Encoder-Only Transformer

As the name suggests, this architectural type uses only the encoder part of the transformer, focusing on encoding the input sequence. For this model type, understanding the input sequence is crucial while generating an output sequence is not required.

Some common applications of an encoder-only transformer include:

Text Classification: It is focused on classifying the input data based on defined parameters. It is often used in email spam filters to categorize incoming emails. The transformer model can also train over the patterns for effective filtration of unwanted messages.

Sentimental Analysis: This feature makes it an appropriate choice for social media companies to analyze customer feedback and their emotion toward a service or product. It provides useful data insights, leading to the creation of effective strategies to enhance customer satisfaction.

Read about sentiment analysis in LLMs

Anomaly Detection: It is particularly useful for finance companies. The analysis of financial transactions allows the timely detection of anomalies. Hence, possible fraudulent activities can be addressed promptly.

Other uses of an encoder-only transformer include question-answering, speech recognition, and image captioning.

2. Decoder-Only Transformer

It is a less common type of transformer model that uses only the decoder component to generate text sequences based on input prompts. The self-attention mechanism allows the model to focus on previously generated outputs in the sequence, enabling it to refine the output and create more contextually aware results.

Some common uses of decoder-only transformers include:

Text Summarization: It can iteratively generate textual summaries of the input, focusing on including the important aspects of information.

Text Generation: It builds on a provided prompt to generate relevant textual outputs. The results cover a diverse range of content types, like poems, codes, and snippets. It is capable of iterating the process to create connected and improved responses.

Chatbots: It is useful to handle conversational interactions via chatbots. The decoder can also consider previous conversations to formulate relevant responses.

Explore the role of attention mechanism in transformers

3. Encoder-Decoder Transformer

This is a classic architectural type of transformer, efficiently handling sequence-to-sequence tasks, where you need to transform one type of sequence (like text) into another (like a translation or summary). An encoder processes the input sequence while a decoder is used to generate an output sequence.

Some common uses of an encoder-decoder transformer include:

Machine Translation: Since the sequence is important at both the input and output, it makes this transformer model a useful tool for translation. It also considers contextual references and relationships between words in both languages.

Text Summarization: While this use overlaps with that of a decoder-only transformer, text summarization differs from an encoder-decoder transformer due to its focus on the input sequence. It enables the creation of summaries that focus on relevant aspects of the text highlighted in an input prompt.

Question-Answering: It is important to understand the question before providing a relevant answer. An encoder-decoder transformer allows this focus on both ends of the communication, ensuring each question is understood and answered appropriately.

Learn how LlamaIndex can be used to build Q&A chatbots

This concludes our exploration of architecture-based transformer models. Let’s explore the classification from the lens of pre-training approaches.

Categorization Based on Pre-Training Approaches

While the architectural differences provide a basis for transformer types, the models can be further classified based on their techniques of pre-training.

Let’s explore the various transformer models segregated based on pre-training approaches.

1. Masked Language Models (MLMs)

Models with this pre-training approach are usually encoder-only in architecture. They are trained to predict a masked word in a sentence based on the contextual information of the surrounding words. The training enables these model types to become efficient in understanding language relationships.

Some common MLM applications are:

Boosting Downstream NLP Tasks: MLMs train on massive datasets, enabling the models to develop a strong understanding of language context and relationships between words. This knowledge enables MLM models to contribute and excel in diverse NLP applications.

General-Purpose NLP Tool: The enhanced learning, knowledge, and adaptability of MLMs make them a part of multiple NLP applications. Developers leverage this versatility of pre-trained MLMs to build a basis for different NLP tools.

Efficient NLP Development: The pre-trained foundation of MLMs reduces the time and resources needed for the deployment of NLP applications. It promotes innovation, faster development, and efficiency.

2. Autoregressive Models

Typically built using a decoder-only architecture, this pre-training model is used to generate sequences iteratively. It can predict the next word based on the previous one in the text you have written. Some common uses of autoregressive models include:

Text Generation: The iterative prediction from the model enables it to generate different text formats. From codes and poems to musical pieces, it can create all while iteratively refining the output as well.

Chatbots: The model can also be utilized in a conversational environment, creating engaging and contextually relevant responses,

Machine Translation: While encoder-decoder models are commonly used for translation tasks, some languages with complex grammatical structures are supported by autoregressive models.

Here’s a list of translation tools for you to explore

3. Conditional Transformer

This transformer model incorporates the additional information of a condition along with the main input sequence. It enables the model to generate highly specific outputs based on particular conditions, ensuring more personalized results.

Some uses of conditional transformers include:

Machine Translation with Adaptation: The conditional aspect enables the model to set the target language as a condition. It ensures better adjustment of the model to the target language’s style and characteristics.

Summarization with Constraints: Additional information allows the model to generate summaries of textual inputs based on particular conditions.

Speech Recognition with Constraints: With the consideration of additional factors like speaker ID or background noise, the recognition process enhances to produce improved results.

Future of Transformer Model Types

While numerous transformer model variations are available, the ongoing research promises their further exploration and growth. Some major points of further development will focus on efficiency, specialization for various tasks, and integration of transformers with other AI techniques.

Transformers can also play a crucial role in the field of human-computer interaction with their enhanced capabilities. The growth of transformers will definitely impact the future of AI. However, it is important to understand the uses of each variation of a transformer model before you choose the one that fits your requirements.

Transformer models are a type of deep learning model that is used for natural language processing (NLP) tasks. They can learn long-range dependencies between words in a sentence, which makes them very powerful for tasks such as machine translation, text summarization, and question answering.

Transformer models work by first encoding the input sentence into a sequence of vectors. This encoding is done using a self-attention mechanism, which allows the model to learn the relationships between the words in the sentence.

Once the input sentence has been encoded, the model decodes it into a sequence of output tokens. This decoding is also done using a self-attention mechanism.

The attention mechanism is what allows transformer models to learn long-range dependencies between words in a sentence. The attention mechanism works by focusing on the most relevant words in the input sentence when decoding the output tokens.

Learn in detail about transformer models here:

Transformer models are very powerful, but they can be computationally expensive to train. However, they are constantly being improved, and they are becoming more efficient and powerful all the time.

History

The history of transformers in neural networks can be traced back to the early 1990s when Jürgen Schmidhuber proposed the first transformer model. This model was called the “fast weight controller” and it used a self-attention mechanism to learn the relationships between words in a sentence. However, the fast-weight controller was not very efficient, and it was not widely used.

In 2017, Vaswani et al. published the paper “Attention is All You Need”, which introduced a new transformer model that was much more efficient than the fast-weight controller. This new model, which is now simply called the “transformer”, quickly became state-of-the-art for a wide range of natural language efficient (NLP) tasks, including machine translation, text summarization, and question answering.

Learn more about NLP in this blog —-> Applications of Natural Language Processing

The transformer has been so successful because it can learn long-range dependencies between words in a sentence. This is essential for many NLP tasks, as it allows the model to understand the context of a word in a sentence. The transformer does this using a self-attention mechanism, which allows the model to focus on the most relevant words in a sentence when decoding the output tokens.

The transformer has had a major impact on the field of NLP. It is now the go-to approach for many NLP tasks, and it is constantly being improved. In the future, transformers are likely to be used to solve a wider range of NLP tasks, and they will become even more efficient and powerful.

Here are some of the key events in the history of transformers in neural networks:

• 1990: Jürgen Schmidhuber proposes the first transformer model, the “fast weight controller”.
• 2017: Vaswani et al. publish the paper “Attention is All You Need”, which introduces the transformer model.
• 2018: Transformer models achieve state-of-the-art results on a wide range of NLP tasks, including machine translation, text summarization, and question answering.
• 2019: Transformers are used to create large language models (LLMs) such as BERT and GPT-2.
• 2020: LLMs are used to create even more powerful models such as GPT-3.

The history of transformers in neural networks is still being written. It is an exciting time to be in the field of NLP, as transformers are making it possible to solve previously intractable problems.

NLP transformer architecture

The transformer model is made up of two main components: an encoder and a decoder. The encoder takes the input sentence as input and produces a sequence of vectors. The decoder then takes these vectors as input and produces the output sentence.

The encoder consists of a stack of self-attention layers. Each self-attention layer takes a sequence of vectors as input and produces a new sequence of vectors. The self-attention layer works by first computing a score for each pair of words in the input sequence. The score for a pair of words is a measure of how related the two words are. The self-attention layer then uses these scores to compute a weighted sum of the input vectors. The weighted sum is the output of the self-attention layer.

The decoder consists of a stack of self-attention layers and a recurrent neural network (RNN). The self-attention layers work the same way as in the encoder. The RNN takes the output of the self-attention layers as input and produces a sequence of output tokens. The output tokens are the words in the output sentence.

The attention mechanism is what allows the transformer model to learn long-range dependencies between words in a sentence. The attention mechanism works by focusing on the most relevant words in the input sentence when decoding the output tokens.

For example, let’s say we want to translate the sentence “I love you” from English to Spanish. The transformer model would first encode the sentence into a sequence of vectors. Then, the model would decode the vectors into a sequence of Spanish words. The attention mechanism would allow the model to focus on the words “I” and “you” in the English sentence when decoding the Spanish words “te amo”.

Transformer models are a powerful tool for NLP, and they are constantly being improved. They are now the go-to approach for many NLP tasks, and they are constantly being improved.

Encoding and Decoding

Encoding and decoding are two key concepts in natural language processing (NLP). Encoding is the process of converting a sequence of words into a sequence of vectors. Decoding is the process of converting a sequence of vectors back into a sequence of words.

Encoding

The encoder in a transformer model takes a sequence of words as input and produces a sequence of vectors. The encoder consists of a stack of self-attention layers. Each self-attention layer takes a sequence of vectors as input and produces a new sequence of vectors. The self-attention layer works by first computing a score for each pair of words in the input sequence. The score for a pair of words is a measure of how related the two words are. The self-attention layer then uses these scores to compute a weighted sum of the input vectors. The weighted sum is the output of the self-attention layer.

For example, let’s say we have the sentence “I like you”. The encoder would first compute a score for each pair of words in the sentence. The score for the word “I” and the word “like” would be high, because these words are related. The score for the word “like” and the word “you” would also be high, for the same reason. The encoder would then use these scores to compute a weighted sum of the input vectors. The weighted sum would be a vector that represents the meaning of the sentence “I like you”.

Decoding

The decoder in a transformer model takes a sequence of vectors as input and produces a sequence of words. The decoder also consists of a stack of self-attention layers. The self-attention layers work the same way as in the encoder. The decoder also has an RNN, which takes the output of the self-attention layers as input and produces a sequence of output tokens. The output tokens are the words in the output sentence.

For example, let’s say we want to translate the sentence “I love you” from English to Spanish. The decoder would first take the vector that represents the meaning of the sentence “I love you” as input. Then, the decoder would use the self-attention layers to compute a weighted sum of the input vectors. The weighted sum would be a vector that represents the meaning of the sentence “I love you” in Spanish. The decoder would then use the RNN to produce a sequence of Spanish words. The output of the RNN would be the Spanish sentence “Te amo”

Encoder only models

Encoder-only models are a type of transformer model that only has an encoder. Encoder-only models are typically used for tasks like text classification, where the model only needs to understand the meaning of the input text.

For example, an encoder-only model could be used to classify a news article as either “positive” or “negative”. The encoder would first encode the article into a sequence of vectors. Then, the model would use a classifier to classify the article.

Encoder-only models are typically less powerful than full transformer models, but they are much faster and easier to train. This makes them a good choice for tasks where speed and efficiency are more important than accuracy.

Decoder only models

Decoder-only models are a type of transformer model that only has a decoder. Decoder-only models are typically used for tasks like machine translation, where the model needs to generate the output text.

For example, a decoder-only model could be used to translate a sentence from English to Spanish. The decoder would first take the English sentence as input. Then, the decoder would use the self-attention layers to compute a weighted sum of the input vectors. The weighted sum would be a vector that represents the meaning of the sentence in Spanish. The decoder would then use an RNN to produce a sequence of Spanish words. The output of the RNN would be the Spanish sentence.

Decoder-only models are typically less powerful than full transformer models, but they are much faster and easier to train. This makes them a good choice for tasks where speed and efficiency are more important than accuracy.

Here is a table that summarizes the differences between encoder-only models and decoder-only models:

What are transformer models built of

Transformer models are built of the following components:

• Embedding layer: The embedding layer converts the input text into a sequence of vectors. The vectors represent the meaning of the words in the text.
• Self-attention layers: The self-attention layers allow the model to learn long-range dependencies between words in a sentence. The self-attention layers work by computing a score for each pair of words in the sentence. The score for a pair of words is a measure of how related the two words are. The self-attention layers then use these scores to compute a weighted sum of the input vectors. The weighted sum is the output of the self-attention layer.
• Positional encoding: The positional encoding layer adds information about the position of each word in the sentence. This is important for learning long-range dependencies, as it allows the model to know which words are close to each other in the sentence.
• Decoder: The decoder takes the output of the self-attention layers as input and produces a sequence of output tokens. The output tokens are the words in the output sentence.

Transformer models are also typically trained with the following techniques:

• Masked language modeling: Masked language modeling is a technique used to train transformer models to predict the missing words in a sentence. This helps the model to learn to attend to the most relevant words in a sentence.
• Attention masking: Attention masking is a technique used to prevent the model from attending to future words in a sentence. This is important for preventing the model from learning circular dependencies.
• Gradient clipping: Gradient clipping is a technique used to prevent the gradients from becoming too large. This helps to stabilize the training process and prevent the model from overfitting.

Attention layers are a type of neural network layer that allows the model to learn long-range dependencies between words in a sentence. The attention layer works by computing a score for each pair of words in the sentence. The score for a pair of words is a measure of how related the two words are. The attention layer then uses these scores to compute a weighted sum of the input vectors. The weighted sum is the output of the attention layer.

The input to the attention layer is a sequence of vectors. The output of the attention layer is a weighted sum of the input vectors. The weights are computed using the scores for each pair of words in the sentence.

The attention layer can learn long-range dependencies because it allows the model to attend to any word in the sentence, regardless of its position. This is in contrast to recurrent neural networks (RNNs), which can only attend to words that are close to the current word.

Transformer architecture is a neural network architecture that is based on attention layers. Transformer models are typically made up of an encoder and a decoder. The encoder takes the input text as input and produces a sequence of vectors. The decoder takes the output of the encoder as input and produces a sequence of output tokens.

The encoder consists of a stack of self-attention layers. The decoder also consists of a stack of self-attention layers. The self-attention layers in the decoder can attend to both the input text and the output text. This allows the decoder to generate the output text in a way that is consistent with the input text.

Transformer models are typically trained with the masked language modeling technique. Masked language modeling is a technique used to train transformer models to predict the missing words in a sentence. This helps the model to learn to attend to the most relevant words in a sentence.