1. The Big Idea Behind the Transformer

Instead of reading a sentence word-by-word as in an RNN, the Transformer reads the whole sentence in parallel. This alone dramatically speeds up training.

But then the natural question would be:

• How does the model know which words relate to each other if it is seeing everything at once?
• This is where self-attention kicks in.
• Self-attention allows the model to dynamically calculate the importance scores of other words in the sequence. For instance, in the sentence:

“The cat which you saw yesterday was sleeping.”

When predicting something about “cat”, the model can learn to pay stronger attention to “was sleeping” than to “yesterday”, because the relationship is more semantically relevant.

Transformers do this kind of reasoning for each word at each layer.

2. How Self-Attention Actually Works (Human Explanation)

Self-attention sounds complex but the intuition is surprisingly simple:

• Think of each token, which includes words, subwords, or other symbols, as a person sitting at a conference table.

Everybody gets an opportunity to “look around the room” to decide:

• To whom should I listen?
• How much should I care about what they say?
• How do their words influence what I will say next?

Self-attention calculates these “listening strengths” mathematically.

3. The Q, K, V Mechanism (Explained in Human Language)

Each token creates three different vectors:

• Query (Q) – What am I looking for?
• Key (K) – what do I contain that others may search for?
• Value.V- what information will I share if someone pays attention to me?

Analogical is as follows:

• Imagine a team meeting.
• Your Query is what you are trying to comprehend, such as “Who has updates relevant to my task?”
• Everyone’s Key represents whether they have something you should focus on (“I handle task X.”)
• Everyone’s Value is the content (“Here’s my update.”)
• It computes compatibility scores between every Query–Key pair.
• These scores determine how much the Query token attends to each other token.

Finally, it creates a weighted combination of the Values, and that becomes the token’s updated representation.

4. Why This Is So Powerful

Self-attention gives each token a global view of the sequence—not a limited window like RNNs.

This enables the model to:

• Capture long-range dependencies
• Understand context more precisely
• Parallelize training efficiently
• Capture meaning in both directions – bidirectional context

And because multiple attention heads run in parallel (multi-head attention), the model learns different kinds of relationships at once for example:

• syntactic structure
• Semantic Similarity
• positional relationships
• co-reference: linking pronouns to nouns

Each head learns, through which to interpret the input in a different lens.

5. Why Transformers Replaced RNNs and LSTMs

• Performance: They simply have better accuracy on almost all NLP tasks.
• Speed: They train on GPUs really well because of parallelism.
• Scalability: Self-attention scales well as models grow from millions to billions of parameters.

Flexibility Transformers are not limited to text anymore, they also power:

• image models
• Speech models
• video understanding

GPT-4o, Gemini 2.0, Claude 3.x-like multimodal systems

agents, code models, scientific models

Transformers are now the universal backbone of modern AI.

6. A Quick Example to Tie It All Together

Consider the sentence:

• “I poured water into the bottle because it was empty.”
• Humans know that “it” refers to “the bottle,” not the water.

Self-attention allows the model to learn this by assigning a high attention weight between “it” and “bottle,” and a low weight between “it” and “water.”

This dynamic relational understanding is exactly why Transformers can perform reasoning, translation, summarization, and even coding.

Summary-Final (Interview-Friendly Version)

A Transformer is a neural network architecture built entirely around the idea of self-attention, which allows each token in a sequence to weigh the importance of every other token. It processes sequences in parallel, making it faster, more scalable, and more accurate than previous models like RNNs and LSTMs.

Self-attention works by generating Query, Key, and Value vectors for each token, computing relevance scores between every pair of tokens, and producing context-rich representations. This ability to model global relationships is the core reason why Transformers have become the foundation of modern AI, powering everything from language models to multimodal systems.