Understanding Transformers : Encoder, Decoder, and Everything In Between

Until recently, my understanding of AI was rooted in traditional machine learning : logistic regression, random forests, and classification models.

Then the AI world exploded.

Transformers, embeddings, large language models, there was suddenly an entirely new vocabulary to learn. I tried to keep up: reading blogs, running experiments, taking notes, and building small prototypes. But it always felt like puzzle pieces scattered across the table.

So I decided to do something more structured.

I enrolled in a Data Science and AI course on Udemy, one that covers everything from core ML to the latest in Generative AI. And as I go through it, I want to document what I’m learning here to connect those scattered dots, and maybe help others who are walking through the same maze of curiosity and confusion.

This week’s topic? One of the most fundamental ideas behind modern AI: Transformers.

What Exactly Are Transformers?

Transformers changed the way machines process information. Before them, models like RNNs and LSTMs could only handle data sequentially processing one word after another, like reading a sentence out loud. That worked, but it was slow and limited in how much context the model could remember.

Transformers broke this pattern with one powerful concept: attention.

Instead of reading word by word, they can look at the entire sequence at once, deciding which parts are relevant to each prediction. It’s like being able to read an entire paragraph and instantly know which sentences carry the meaning no rereading needed.

That simple shift letting the model attend to what matters made it possible to train much larger, smarter systems that understand context deeply and generate text more coherently.

But what I found most interesting while studying this is that “Transformer” isn’t just one single model, it’s a family of architectures that use the same idea differently depending on the task.

There are three types of Transformer Architecture :

Encoder-Only Models : The Understanding Experts

Encoder-only Transformers focus purely on understanding. They read input data, build rich contextual representations, and stop there no generation, no text completion, just deep comprehension.

BERT (Bidirectional Encoder Representations from Transformers) is the perfect example. BERT reads the entire sentence both forward and backward, allowing it to grasp how every word relates to every other word. That’s what “bidirectional” means understanding context from both directions simultaneously.

These models shine at tasks where meaning matters more than output:

• Sentiment analysis (Is this review positive or negative?)

• Classification (What category does this text belong to?)

• Named Entity Recognition (Which parts of a sentence refer to names, locations, etc.?)

• Semantic search or embeddings (Finding similar meanings, not just similar words)

When you use models that “understand” text like for content moderation, search ranking, or document tagging you’re often seeing an encoder-only Transformer at work behind the scenes.

2. Decoder-Only Models : The Generative Storytellers

Decoder-only models are built for generation. Unlike encoders, which analyze everything upfront, decoders generate one token at a time each word informed by everything that came before it.

The most famous examples are GPT models (Generative Pretrained Transformers) the ones powering ChatGPT and Copilot.

These models take an input (a prompt), process it, and predict the next most likely word over and over again until they form a coherent output. That’s why they excel at creative, open-ended tasks like:

• Writing essays, code, or poetry

• Completing sentences or stories

• Powering conversational assistants

• Summarizing long content in natural language

In a way, decoder-only Transformers are like improvisational speakers they listen to what’s been said, draw from experience, and continue the thought seamlessly.

What makes them powerful isn’t just their training scale it’s their ability to carry context forward while staying coherent and adaptable, even across long conversations.

3. Encoder-Decoder Models : The Translators

Encoder-decoder models combine the best of both worlds. The encoder reads and understands the input, while the decoder takes that understanding and turns it into something new.

Imagine a translator: one side listens to English, the other speaks fluent French. That’s exactly how T5, BART, and Whisper work.

The encoder digests the input into a rich contextual embedding, and the decoder uses that representation to generate output often in a different format or language.

These models are the backbone of many real-world applications:

• Translation (e.g., English → German)

• Summarization (Long document → Concise summary)

• Question answering (Context → Answer)

• Speech-to-text or text-to-speech tasks

What I find most fascinating is how structured this feels like a pipeline with two minds: One that understands, and One that expresses.

You can almost think of encoder-decoder architectures as collaborations between comprehension and creativity one half builds meaning, the other half communicates it.

My Reflection

Studying Transformers made me realize that this architecture isn’t just a technical innovation it’s a framework for thinking about intelligence itself.

Encoders specialize in understanding, decoders in expression, and encoder-decoders in transformation. Together, they mirror the way humans process and communicate information.

We read. We think. We respond.

And when you look closely, that’s exactly what a Transformer does only faster, deeper, and at a scale that’s hard to imagine.

I’m only scratching the surface here, but understanding this foundation suddenly makes the rest of AI feel a lot less abstract. Every new model I read about GPT, T5, Whisper now feels like a variation on a theme I finally understand.

Next up, I’ll be diving into the attention mechanism the real magic that lets Transformers decide what to focus on. From what I can tell, that’s where things start getting really interesting.