Image: An aerial view of a lush forest meeting a blue sea – a metaphor for the vast context that modern AI models must “scan” and interpret (attention enables models to look at all parts of a sequence at once).

By Lakruwan Priyankara, Senior Software Engineer (AI)

The recent explosion of Large Language Models (LLMs) like GPT-4, Claude, Gemini and others has dramatically reshaped the AI landscape. In the last few years, AI systems have gone from struggling with simple tasks to generating human-like text, answering complex questions, and even processing images and audio. These breakthroughs are all powered by transformers: a new kind of neural network architecture that underpins every modern LLM. To truly understand or build such models, one must grasp the transformer’s mechanics and why it replaced earlier approaches. The transformer’s self-attention mechanism and scalable design are not just academic curiosities—they are the key enabler that let today’s generative AIs reach unprecedented capability and breadth of understanding.

From Simple RNNs to LSTMs and GRUs

Before transformers, AI text models were built on recurrent neural networks (RNNs) and their variants. An RNN processes words one after the other, updating a hidden “memory” state at each step. You can imagine an RNN as a reader going through a sentence word by word, carrying forward a scribbled note that summarizes what it has seen so far. However, this simple design has critical flaws: it struggles to remember far-apart information and is inherently sequential.

  • Long-range dependencies: In practice, plain RNNs “forget” older context as they read. This is partly due to vanishing gradients during training – the influence of early words decays exponentially, so distant relationships (like the subject of a sentence far back) are lost. (Analogously, imagine trying to summarize page one of a book by the time you finish page fifty – old details slip away.)
    Example:
    Consider the sentence:
    “I grew up in France… I speak fluent ______.”

    To accurately predict the missing word (“French”), the model must retain the information that the subject grew up in France, which was mentioned several words earlier. Standard RNNs often fail to maintain such long-term dependencies, leading to incorrect predictions.

  • Sequential bottleneck: Since each step depends on the previous hidden state, RNNs cannot parallelize computation across the sequence. You must read word 1, then word 2, and so on. This makes training slow, especially on long documents. For example, summarizing a short paragraph may be feasible, but running an RNN over an entire page or book is prohibitively time-consuming.
  • Explosion/vanishing of gradients: Training RNNs over long sequences often leads to exploding or vanishing gradients, hampering learning. (LSTMs and GRUs were introduced to partly address this with gating.)

To mitigate some of these issues, more sophisticated recurrent variants were developed:

  • LSTMs (Long Short-Term Memory): Introduced memory cells with input, output, and forget gates to preserve information longer. Intuitively, an LSTM is like a reader who uses sticky notes: if an earlier fact is important (e.g. “Tom is a cat”), the model writes it on a note and carries it forward. However, LSTMs still update sequentially and can only extend memory by a limited amount. They helped with moderate-distance dependencies, but didn’t fully solve the problem of extremely long context or parallel training.
  • GRUs (Gated Recurrent Units): A simpler alternative to LSTM with update and reset gates. GRUs also help selective memory, but remain fundamentally sequential.

In summary, the pre-transformer era of RNNs (including LSTM/GRU variants) laid the foundation for sequential text modeling, but faced core limitations: difficulty capturing very long-range context and slow, non-parallel training. They treated language like a strict chain, remembering only the tail of the chain at each step. As models grew and datasets exploded, these bottlenecks became untenable.

The Transformer Breakthrough (2017)

In 2017, Vaswani et al. introduced the Transformer (“Attention Is All You Need”), a radically different architecture. The key insight was to discard recurrence and convolution entirely and rely solely on self-attention. In a transformer, every token in the input can “attend” to (i.e. directly use information from) every other token in the same sequence. No more one-step-at-a-time reading.

  • Self-attention mechanism: Each word (or token) produces query (Q), key (K), and value (V) vectors. The attention output for a given token is essentially a weighted sum of all value vectors, where weights come from dot-products between the token’s query and all keys. Intuitively, the model is checking “how relevant is each other word (key) to this word (query)?” and uses that to blend the values.
  • Multi-head attention: The transformer uses multiple attention “heads” in parallel, allowing it to consider different aspects of inter-token relationships at once.
  • Positional encoding: Since transformers lack recurrence, they inject sequence order explicitly. Each position gets a fixed or learned “positional encoding” vector added to its embedding. This way, the model knows “word 5 comes after word 4,” enabling it to handle word order and sequence structure.
  • Encoder-decoder structure: The original transformer has an encoder stack (reading the input) and a decoder stack (generating output), suitable for tasks like translation. (Modern LLMs often use just the decoder part in a generative fashion, or just the encoder in a bidirectional way like BERT.)

Conceptually, self-attention is like reading an entire paragraph at once but letting each word “look around” to gather clues. One analogy: if you have a book, an RNN reads it page by page linearly. In contrast, a transformer is like skimming the whole chapter, allowing any sentence to directly reference any other. For example, when processing the sentence “The bank will not approve the loan,” the word “bank” can attend to “loan” (and vice versa) in one step, helping to disambiguate whether “bank” means financial institution or river bank. This global view lets the model capture context from far away in just a few layers, rather than years of recurrences.

Scaling Up: Parallelism and Contextual Power

By enabling full parallelization over sequences, transformers unlocked massive model scaling. Unlike RNNs, transformers can process a whole sequence in parallel during both training and inference. This leads to two crucial advantages:

  • Massive parallelism: Transformers allow GPUs to do matrix operations on entire sequences at once. As AWS notes, this parallelism “enables transformers to train and process longer sequences in less time than an RNN does” and supports order-of-magnitude larger models. Backpropagation is no longer bottlenecked to step-by-step, so gradient information flows freely and efficiently. In practice, this means we can train models with billions or trillions of parameters on enormous text corpora.
  • Contextual depth: With each token attending to every other, transformers build richer internal representations. A deep transformer with many layers can relate distant concepts over and over, effectively growing its “contextual receptive field.” This results in much better handling of long-range dependencies and nuanced language phenomena. In short, transformers don’t forget earlier context – they explicitly compare all parts of the input.

Together, these features let transformer-based models achieve far better performance on NLP tasks and scale linearly with compute (subject to quadratic attention costs). The model bottleneck shifts from memorizing data to designing efficient architectures (hence all the research on sparse attention, compression, etc., discussed below).

From BERT and GPT to Modern LLMs

The original Transformer was a general framework. Its descendants reshaped NLP:

  • BERT (Bidirectional Encoder, Devlin et al. 2018): BERT took the transformer encoder stack and pre-trained it on massive text corpora with a masked-language objective. It learned to fill in blanks by looking at both left and right context (hence “bidirectional”). Fine-tuned BERT models became state-of-the-art on many tasks like question-answering and sentiment analysis. Crucially, BERT showed that pre-training a deep transformer on raw text, then fine-tuning, was extremely effective.
  • GPT (Generative Pretrained Transformer, OpenAI 2018 onward): GPT models use a transformer decoder stack in a left-to-right (autoregressive) fashion. GPT-1/2/3 and GPT-4 were trained to predict the next token on massive data. This unlocked remarkably powerful generation. OpenAI’s GPT-4 and successors (now including GPT-4o with multimodal input and GPT-4.1 with 1 million-token context) illustrate the trend of ever-larger, more capable generative transformers. IBM notes that GPT’s transformer backbone has powered many developments since 2017.
  • Other models: Anthropic’s Claude, Google’s Gemini (formerly Bard), Meta’s LLaMA/GPT-3 derivatives, and others are also built on transformer variants. For example, Gemini 2.5 recently improved long-context reasoning and efficiency, leveraging transformer innovations.

Building on the basic transformer, researchers have pushed its capabilities:

  • Sparse Attention & Long-Context Models: The standard full attention is O
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, so for very long inputs (thousands of tokens), engineers use sparse or structured attention. Models like Longformer, Reformer, Big Bird, etc., attend only to local windows or selected tokens. GPT-4.1 even boasts a 1 million token context window by using mixture-of-experts and memory techniques. Sparse attention architectures allow LLMs to read entire books or conversations by focusing on key portions.
  • Mixture of Experts (MoE): These models increase parameter count by having multiple “expert” subnetworks. Each input token is routed to a subset of experts, so the model can be vastly wider without a proportional compute increase. As Hugging Face explains, MoE transformers replace a dense feed-forward network with many parallel “experts,” chosen by a learned gate. This enables enormous models (trillions of parameters) where only a few parts activate per token. Google’s GShard and Switch Transformers pioneered MoE, and recent LLMs like GPT-4 use some expert/mixer designs for efficiency.
  • Memory-Augmented Transformers: To overcome fixed context limits, many systems integrate external memory or retrieval. For example, Retrieval-Augmented Generation (RAG) techniques fetch relevant documents into the transformer’s context. Other approaches build persistent memory vectors or key-value caches. These methods effectively give the model an “infinite context,” going beyond what pure self-attention can hold.
  • Multimodal Transformers: The same attention machinery extends beyond text. GPT-4o, Gemini, Claude 3, and others fuse vision (images), audio, and more. They often use a unified transformer stack to process tokens from different modalities. For instance, an image is split into patch tokens and fed alongside text tokens; the transformer then attends across modalities. These multimodal giants are basically transformers on steroids, demonstrating that understanding one modality (language) generalizes to others with the same core architecture.
  • State-Space & Recurrent Alternatives: Very recent work explores replacing attention altogether. Models like Mamba use state-space layers (inspired by control theory) for token interaction. These aim for O
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time instead of quadratic, addressing even longer contexts and faster inference. While not mainstream yet, such state-space Transformers show that even our best architecture is still evolving.

Throughout this evolution, diagrams and analogies often help. For example, one can visualize attention as a fully connected graph over tokens (each word linked to every other), in contrast to a chain for RNNs. Or use a simple chart showing that RNNs update sequentially (one arrow at a time) versus transformers updating all positions in parallel (many arrows at once). These illustrations clarify why transformers scale in a way RNNs couldn’t.

The Takeaway: Transformers are the Key to LLMs

In conclusion, the modern era of LLMs is built entirely on the Transformer paradigm. Early models (GPT, BERT) proved its power on text, and every recent advance—sparse attention, mixture-of-experts, multimodal fusion, long-term memory, and even state-space experiments—either extends or replaces parts of the transformer framework. Whether you are fine-tuning a model or designing a new one, understanding how transformers work under the hood is essential. Their self-attention mechanism and architecture dictate how information flows, how context is captured, and how compute is utilized. Master these fundamentals, and you’ll have the toolkit to innovate with the latest and future LLMs. Conversely, without that understanding, LLMs will seem like magic black boxes.

For anyone serious about AI today, learning transformers isn’t optional – it’s the foundation of the field. LLMs stand on transformers’ shoulders, so to see farther in this landscape, one must first stand on that solid base.

Key References: Vaswani et al. (2017) introduced the Transformer. AWS docs summarize RNN vs transformer tradeoffs. HuggingFace blogs explain Mixture-of-Experts. OpenAI’s blog covers GPT-4.1’s million-token context.