I want to walk you through one of the most important breakthroughs in modern artificial intelligence. The model family called Transformers changed everything about how machines read, understand, and generate language. In this article I explain why Transformers were invented, how they work, and why they sit at the core of systems like GPT, BERT, LLaMA, Claude, and Gemini. I will start from the basics and build up step by step so you can see the full story from simple neural networks to the powerful attention based architecture that powers today's most generative AI systems.
Why we needed a new architecture?
When I first learned about sequence processing in AI I noticed a consistent pattern. Early neural networks were great at classifying static inputs like images or tabular data. But language is not a static object. Language unfolds as a sequence. Words depend on earlier words and sometimes on words that appeared many steps before. If a model cannot remember or focus selectively across the whole sequence, it will lose important context. That is the problem Transformers were built to solve.
Transformers came into the world to overcome two main limitations. First, earlier models struggled to carry long distance context. Second, those models were often slow to train because they processed tokens one by one. Transformers solved both problems by introducing a powerful mechanism called attention and by processing sequences in parallel. That single change unlocked much larger models, faster training, and far better handling of long context. That is why Transformers now power nearly every large language model and many other AI systems.
Machine learning and deep learning
Image credits: ResearchGate.Net
Let me set the scene by explaining where Transformers sit in the big picture. Artificial intelligence is a broad field. Within it, machine learning is the branch that gives machines the ability to learn from data rather than follow explicitly coded rules. Within machine learning, deep learning is a specialization that uses multi layer artificial neural networks to learn complex patterns from large datasets. Transformers are an architecture within deep learning. They are a specific neural network design that excels at dealing with sequences such as text and speech.
Machine learning has three common learning paradigms that are worth recalling because they influence how models are trained and used.
- Supervised learning: The model learns from labeled examples. For example, you show many images labeled cat or not cat. The model learns the mapping from image to label and can then predict on new images.
- Unsupervised learning: The model finds structure in unlabeled data. Clustering customers by behavior or learning useful vector representations of words are typical examples.
- Reinforcement learning: The model learns by trial and error, maximizing rewards. This is common in game playing or robotics where actions lead to feedback signals.
Artificial neural networks (ANNs) and their limitations
Artificial neural networks, or ANNs, are inspired by the brain. They consist of neurons arranged in layers. Each neuron receives inputs, computes a weighted sum, applies a non linear function, and passes a signal forward. Classic feed forward networks work well for image recognition and many other tasks where the entire input can be treated as a static snapshot.
However feed forward ANNs have a key limitation when it comes to language. They do not have a built-in mechanism to remember earlier words. If you present a sentence to a feed forward network, it sees the sentence as a fixed vector. It does not inherently model sequences or temporal dependencies. Language is not a collection of isolated tokens. Words interact over time. For instance consider the pair dog bites man and man bites dog. The same words appear in both phrases but the meaning is inverted by order. Feed forward methods do not track order naturally. That is why sequence specific models were developed.
Recurrent neural networks (RNNs) and the memory problem
Image credits: GeeksForGeeks
Recurrent neural networks, or RNNs, were the first widely used family of models designed for sequential data. The core idea is intuitive. Rather than treating the input as a static vector, an RNN reads tokens one at a time and maintains a hidden state or memory vector that summarizes what it has seen so far. Each new token updates the hidden state. This memory is then used to predict the next token or the output label. RNNs therefore give the model a way to remember previous context as the sequence unfolds.
RNNs were a major step forward, but they had two serious drawbacks.
Vanishing and exploding gradients. When training RNNs with long sequences, gradients that propagate back through many steps tend to vanish or explode, making it hard to learn long range dependencies. Variants like LSTM and GRU mitigated this, but the core issue remained challenging.
Sequential computation. RNNs process tokens one by one. This sequential nature makes training slow and prevents efficient parallelization on modern hardware. As models grew larger and datasets exploded, this became a severe bottleneck.
So we had a class of models that could remember, but only for a limited number of steps, and they were slow to train. A new idea was needed. That idea is attention.
Attention: the key idea
Image credits: Wikipedia
Attention is a mechanism that allows a model to look selectively at different parts of the input sequence when producing each output. Instead of relying solely on a single hidden state to carry all past information, attention lets the model compute a direct measure of relevance between any two tokens in the sequence. It answers a simple question for every pair of tokens: how much should token A pay attention to token B?
Why is that powerful? Because attention breaks the sequential bottleneck and allows the model to connect distant tokens directly. Consider the sentence The cat sat on the mat and it was fluffy. When interpreting the word it, attention helps the model link it directly to cat even though the tokens between them might be several steps long. This alleviates the forgetting problem that RNNs faced.
A key property of attention is parallelism. Attention computations can be done for all token pairs in parallel. This enables much faster training on modern GPUs and TPUs. Attention also makes it easier to scale to very large models and very long sequences.
Attention is All You Need
That phrase comes from the landmark 2017 paper ‘Attention is All You Need’ that introduced the Transformer architecture. The paper showed that a model built entirely around attention, without recurrent operations, could match or beat prior sequence models on machine translation and other tasks. Crucially, the paper demonstrated that attention based models are faster to train and scale better.
Let's dive into Transformers
At a high level, a Transformer is a neural network architecture that relies primarily on attention mechanisms to process sequences. It replaces the recurrent parts of previous models with attention based blocks and feed forward networks wrapped with normalization and residual connections. Transformers operate on the entire sequence at once and learn relationships between tokens through attention.
A Transformer typically has two major components in the original design: an encoder and a decoder. The encoder reads and builds a representation of the input. The decoder generates the output sequence based on that representation. Many modern variants use only the encoder or only the decoder depending on the task. For example, BERT is encoder only and is used for understanding tasks. GPT models are decoder only and are focused on generation. The general architecture and the attention concept are shared across all these variants.
High level flow
Here is the simplified flow you can keep in mind.
- Input tokens are converted into embeddings, numeric vectors that capture meaning.
- Positional information is added to embeddings so the model knows token order.
- The encoder applies stacked layers of multi head self attention and feed forward networks to produce contextualized representations.
- The decoder uses masked self attention to generate tokens step by step while also attending to the encoder outputs to ground generation on the input.
- The final decoder output is passed through a linear layer and softmax to convert scores into probabilities for the next token.
Key components of Transformers
To understand Transformers in more detail, I will break down the most important pieces and explain what each does and why it matters.
1. Token embeddings and positional encoding
Text is discrete and machines need numbers. The first step is to convert each token into a vector. Embeddings capture word meaning in continuous space. Similar words or words that appear in similar contexts end up with similar vectors.
Transformers process the entire sequence in parallel, so they need explicit information about token order. That is the role of positional encoding. We add a positional vector to each token embedding. This combined vector tells the model both what the token is and where it is in the sequence. Without positional signals the model would not be able to distinguish dog bites man from man bites dog.
2. Self attention and scaled dot product
The core operation inside Transformers is self attention. For each token we compute three vectors: the query, the key, and the value. Queries and keys are used to compute attention scores that tell us how much one token should attend to another. Values carry the information that will be combined weighted by those attention scores.
Mathematically, we take the dot product of the query for token i with the key for token j, scale the result, and apply softmax across j to get attention weights. Those weights are used to compute a weighted sum of the value vectors, producing a new representation for token i that incorporates information from other tokens. This is done in parallel for all tokens.
3. Multi head attention
Multi head attention means we compute several independent attention operations in parallel and then concatenate their outputs. Each attention head can focus on different types of relationships. For example one head might learn to track subject verb agreement while another head learns to attach pronouns to their referents. Multiple heads give the model richer, more diverse ways to relate tokens.
4. Add and norm
Residual connections and normalization are critical for training deep models. After each attention or feed forward block we add the block input to the block output and normalize the result. This stabilizes gradients and enables training much deeper stacks of layers. Conceptually, add and norm helps the model combine new transformed information with the original signal while keeping the training dynamics stable.
5. Feed forward networks
Each Transformer layer contains a position wise feed forward network. This is a small two layer neural network applied independently to each position. It increases the model capacity by allowing non linear transformation of each token representation. Feed forward layers are applied after attention and help the model refine the contextualized representation.
6. Masked attention in the decoder
When generating sequences autoregressively, the model should not peek at future tokens. The decoder uses masked self attention so each position can only attend to previous positions and itself. This prevents cheating and ensures the model learns to predict the next token from what it has generated so far.
7. Cross attention from decoder to encoder
In the encoder decoder design, the decoder includes attention layers that attend to encoder outputs. This cross attention step lets the decoder use the encoder representation of the input as context while generating output. It is the mechanism by which the decoder grounds its generation on the input sequence.
8. Final linear and softmax
After the decoder produces the final contextualized vectors, a linear projection maps those vectors to vocabulary sized logits. Softmax converts the logits into probabilities over the vocabulary. The highest probability token is chosen as the next output, or a sampling strategy can be used to introduce diversity.
Putting it all together: encoder and decoder
Let me summarize the encoder and decoder roles in concrete terms.
Encoder: Takes the input sequence, converts tokens to embeddings, adds positional information, and applies N stacked layers of multi head self attention followed by feed forward networks. The encoder outputs a set of contextualized vectors, one per input token. Those vectors capture how each token relates to others in the input.
Decoder: Starts with output token embeddings plus positional encoding. It uses masked self attention to process the partial output sequence generated so far. Then it uses multi head cross attention to attend to the encoder outputs. It further refines the combined information with feed forward layers and finally produces logits that are converted to probabilities for the next token.
Repeat these blocks and stack many layers. Each layer refines the representation, enabling complex features and long range dependencies to be captured. That is the power of deep Transformers.
Why Transformers are so effective
I can condense the reasons why Transformers succeeded into a few connected points.
Parallelism. Unlike RNNs, Transformers process all tokens simultaneously. This unlocks massive speedups on GPUs and TPUs, making it feasible to train on very large datasets.
Direct long range interactions. Attention connects any pair of tokens directly, so models can capture relationships across long distances without needing to propagate information through many intermediate steps.
Scalability. Transformers scale well with model size and data. Increasing layers, hidden sizes, and heads generally leads to better performance when sufficient data and compute are available.
Flexibility. The same architecture can be applied to language, vision, audio, and multimodal tasks. The only changes necessary are tokenization and sometimes positional encodings.
Interpretability. Attention weights provide a rough, often useful signal about which tokens a model is focusing on. While not a definitive explanation tool, attention maps give us intuition about the model behavior.
Common analogies to understand attention and Transformers
I like using a few simple analogies to make intuition stick.
- Reading a paragraph. When you read a paragraph, you do not reread every previous sentence in order to understand the current sentence. Your mind jumps to the most relevant earlier lines. Attention does the same. It lets the model jump to the most relevant tokens.
- Searchlight. Think of attention as a searchlight that shines on relevant words. Multi head attention is multiple searchlights, each tuned to a different pattern such as subject tracking, negation detection, or coreference resolution.
- Index cards on a table. Imagine laying all words out as index cards. Instead of stacking them and reading sequentially, you can scan across the table and pick the exact card you need. Transformers make it possible to scan the whole table at once.
Concrete examples
Examples cement understanding. Consider the simple sentence: ‘The cat sat on the mat and it was fluffy’. When the model generates the token, direct connections will allow the model to link it back to the cat token even though several tokens separate them.
Another example is translating a long sentence where the verb in the first clause must agree with a subject in a much later clause. RNNs struggled to retain that subject information across many steps. Transformers handle this by letting the decoder attend directly to the subject token in the encoder outputs.
Finally, consider tasks where relationships are non local. For instance in code generation, a function defined early can be called much later. Attention enables the model to relate the call site and the definition directly.
Variants and modern practice
Although I described the original encoder decoder Transformer, modern systems vary.
- Encoder only: Models like BERT use only the encoder. They are trained to produce high quality contextualized representations and are suited for classification, question answering, and feature extraction tasks.
- Decoder only: Models like GPT use only the decoder and are trained autoregressively to predict the next token. These models are natural for generation tasks like chat and story writing.
- Encoder decoder with modifications: Machine translation and many sequence transduction tasks still use encoder decoder Transformers, often with task specific adjustments.
- Sparse and efficient Transformers: Researchers are working on variants that reduce the quadratic cost of attention with respect to sequence length, enabling longer context windows at lower compute cost.
Practical implications
The arrival of Transformers led directly to the era of large language models. Because Transformers scale effectively, researchers built increasingly large models trained on web scale data. Those models exhibit surprising capabilities in translation, summarization, question answering, code generation, and more. A few practical consequences are worth noting.
- Foundation models: Large pre trained Transformer based models serve as foundations that can be fine tuned or prompted for many downstream tasks.
- Transfer learning: Pre training on large unlabeled corpora followed by supervised fine tuning or prompt engineering unlocked rapid progress across NLP tasks.
- Multimodality: Transformers can be extended to multiple modalities simply by changing tokenization. Vision Transformers treat image patches as tokens, enabling a unified architecture across text and vision.
- Computation and cost: The flip side of scaling is cost. Training large Transformers is expensive and energy intensive. This has pushed work on efficient architectures, distillation, and parameter efficient fine tuning.
From Transformers to Production: The Role of Data Infrastructure
While Transformers revolutionized how models process language, deploying these systems at scale introduces a critical challenge: managing the embeddings they produce. When models like GPT or BERT convert text into vector representations, those embeddings need to be stored, searched, and combined with enterprise data in real time. This is where specialized data infrastructure becomes essential.
SingleStore addresses this challenge by providing a unified platform that handles both vector embeddings and traditional enterprise data. The platform offers indexed Approximate Nearest Neighbor search that delivers up to 1000x faster vector search performance compared to precise methods, making it practical to search through millions of embeddings in milliseconds.
For generative AI applications, SingleStore enables Retrieval Augmented Generation, a pattern where relevant enterprise data is matched against user queries using semantic search before being sent to language models. This grounds Transformer-based systems in factual, company-specific information and reduces hallucinations.
The platform combines vector similarity search with full-text search, SQL analytics, and support for multiple data types including JSON and time-series data. It integrates with leading AI frameworks like LangChain, OpenAI, Hugging Face, and AWS Bedrock, simplifying the path from prototype to production.
Through SingleStore Notebooks, developers can prototype AI applications using familiar Jupyter-style interfaces while maintaining enterprise-grade security and performance. This bridges the gap between the theoretical power of Transformer architectures and practical deployment requirements that handle real-time data at scale.
Limitations and ongoing challenges
Transformers are powerful, but not perfect. Here are some key limitations and open problems I think about.
Quadratic attention cost: Vanilla attention computes interactions between all token pairs, which scales quadratically with sequence length. For very long contexts this becomes prohibitive.
Data and compute hunger: State of the art performance often requires enormous datasets and massive compute budgets. This limits who can train the largest models from scratch.
Hallucinations and factuality: Generative models can produce fluent but incorrect statements. Attention alone does not guarantee truthfulness.
Interpretability: While attention gives some interpretability, fully understanding why large models produce specific outputs remains challenging.
Summary and final thoughts
In practical terms Transformers brought three major shifts. First they allowed much larger models to be trained efficiently. Second they enabled models to learn complex, long range dependencies that earlier architectures struggled with. Third they provided a flexible framework that can be adapted to many modalities and tasks.
If you take away one point it is this. Attention changed the game. By letting models focus on the most relevant parts of a sequence no matter where they appear, Transformers made machines much better at understanding and generating language.
Know more about Transformers in my in-depth YouTube video.
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