In Post 8 you used transformer embeddings — you called sentence-transformers and OpenAI’s API and got back beautiful context-aware vectors. But we treated the transformer itself as a magic box.
Today we open that box. This is the architecture behind every model you’ve heard of — GPT, Gemini, Llama, BERT, Claude — and the good news is that it’s built from ideas you already understand: embeddings (Post 5–7), neural networks (Post 6), and the “bank vs bank” context problem (Post 7).
What you’ll understand by the end:
- Why RNNs and LSTMs came first — and why they hit a wall
- The 2017 breakthrough: “Attention Is All You Need”
- Self-attention explained with the “money bank” vs “river bank” example
- What Q, K, V (Query, Key, Value) actually are
- The 6-step data flow inside a transformer: tokenize → embed → position → attention → feed-forward
- The encoder/decoder split — and why BERT, GPT, and translation models each use a different piece
📌 This is a concept-first post — the interview-critical mental model, not code. It’s the “why” that makes RAG, fine-tuning, and everything in Module 2 click. Code returns next post with RAG.
📋 Table of Contents
- The Road to Transformers: A 5-Year Timeline
- Why RNNs and LSTMs Hit a Wall
- The Attention Breakthrough (2016–2017)
- Self-Attention: A Word Looking at Its Neighbours
- Query, Key, Value — The Engine of Attention
- Inside a Transformer: The 6-Step Data Flow
- Encoder vs Decoder — BERT, GPT & Translation
- LSTM vs Transformer — The Interview Table
- The Modern LLM Family Tree
- Key Takeaways
Prerequisites
- ✅ Read Post 7 — Word2Vec Hands-On (embeddings + the static “bank vs bank” problem)
- ✅ Read Post 8 — SOTA Embeddings (context-aware vectors, self-attention mentioned)
- ✅ Comfort with the idea of a neural network learning weights (Post 6)
1. The Road to Transformers: A 5-Year Timeline
Transformers didn’t appear from nowhere. They were the finish line of a five-year relay race in NLP, where each model fixed the previous one’s biggest flaw:
| Year | Model | What it added |
|---|---|---|
| ~2014–15 | RNN → LSTM / GRU | Memory for sequence data (Long Short-Term Memory) |
| 2015 | Encoder–Decoder | Sequence-to-sequence (e.g. translation) |
| 2016 | Encoder–Decoder + Attention | Let the decoder “look back” at relevant input words |
| 2016–17 | ULMFiT | Pretrain then fine-tune — scalable transfer learning |
| 2017–18 | Transformer | “Attention Is All You Need” — self-attention, fully parallel |
The punchline: the transformer kept the attention idea, threw away the slow recurrent machinery, and made everything run in parallel. That single move unlocked training on internet-scale data.
2. Why RNNs and LSTMs Hit a Wall
RNNs (Recurrent Neural Networks) and their smarter cousin LSTMs were built for sequence data — text, where order matters. They process words one at a time, passing a “memory” (hidden state) from each step to the next.
LSTMs improved on plain RNNs with gates (input, forget, output) that decide what to remember and what to discard — solving the short-term-memory problem of vanilla RNNs. For a while, LSTM was king of NLP.
But two flaws proved fatal:
- Sequential = slow. Because word 2 depends on word 1’s output, you can’t parallelize. On modern GPUs (built for parallel math), that’s leaving most of the engine idle.
- Long-range memory fades. The vanishing-gradient problem means that by the time an LSTM reaches the end of a long paragraph, it has half-forgotten the beginning. Long sentences → weak context.
🗄️ DBA Analogy — RNN = Row-by-Row Cursor
An LSTM is like processing a table with a cursor: one row at a time, carrying state forward, unable to skip ahead. A transformer is like a set-based query that processes all rows at once. Any DBA knows which one wins on a big table — that’s exactly why transformers scaled and LSTMs didn’t.
3. The Attention Breakthrough (2016–2017)
The first big idea was attention, introduced for machine translation around 2016. Translating “Turn on the TV” into Hindi, the model needed to know which English words matter most for each Hindi word it produces.
Attention let the decoder assign a weight to every input word — literally “pay 80% attention to this word, 15% to that one” — instead of cramming the whole sentence into one fixed memory. Translation quality jumped.
Then in 2017, Google’s paper “Attention Is All You Need” made a radical claim: you don’t need the RNN at all. Keep only attention, apply it within a single sentence (self-attention), run it in parallel — and you get a better, faster model. The transformer was born.
4. Self-Attention: A Word Looking at Its Neighbours
Here’s the one sentence to remember:
Self-attention = a word looking at other words in the same sentence to understand its own meaning.
This is exactly what fixes the “bank” problem from Post 7. Take two sentences:
S1: "money bank grows"
S2: "river bank flows"The word “bank” is identical, but its meaning is opposite. Self-attention resolves this by rebuilding each word’s vector as a weighted blend of every word around it:
In S1, the new vector for "bank" becomes:
bank_new = w1·money + w2·bank + w3·grows
▲
"money" and "grows" pull "bank" toward the FINANCE meaning
In S2:
bank_new = w1·river + w2·bank + w3·flows
▲
"river" and "flows" pull "bank" toward the GEOGRAPHY meaningSame word, different neighbours, different final vector. This is what “dynamic embeddings” means — and it’s the entire reason transformers beat Word2Vec. Another classic example from class:
"Apple launched a new phone while I was eating the apple."
▲ (company) ▲ (fruit)
Self-attention reads the surrounding words and gives each
"apple" the correct, context-appropriate vector.[DIAGRAM: attention heat-map showing “it” attending strongly to “the animal” in “The animal didn’t cross the street because it was too tired”]
5. Query, Key, Value — The Engine of Attention
How does the model compute those weights (w1, w2, w3)? Through three vectors derived from each word, via learned linear transformations. The class used the database-search analogy, which is perfect:
| Term | Role | Search analogy |
|---|---|---|
| Q — Query | What this word is looking for | Your search box text |
| K — Key | What each word offers / advertises | The index / tags on each document |
| V — Value | The actual content passed along | The document you retrieve |
The mechanism: each word’s Query is compared against every word’s Key to produce attention scores (how relevant is each other word?). Those scores become weights, which are applied to the Values and summed. That weighted sum is the word’s new, context-aware representation.
🗄️ DBA Analogy — Q·K is a JOIN on relevance
Think of Q·K like a join condition that scores how well each row (Key) matches your lookup (Query), and V as the columns you actually SELECT. Attention is essentially a soft, weighted join where every word retrieves a blend of every other word — ranked by relevance.
“Multi-head attention” simply runs this process several times in parallel (multiple “heads”), each learning to focus on a different kind of relationship — grammar, meaning, position — then combines them.
6. Inside a Transformer: The 6-Step Data Flow
Here’s what actually happens to your text, start to finish. Take the sentence "Ravi is a mentor":
| # | Step | What happens |
|---|---|---|
| 1 | Tokenization | Split text into tokens (roughly words/sub-words) |
| 2 | Embedding | Each token → a vector (the Word2Vec idea from Post 5, now built-in) |
| 3 | Parallel processing | All tokens enter the network at once — no waiting in line |
| 4 | Positional Encoding (PE) | Add order information — since everything’s parallel, the model needs to be told word order |
| 5 | Self-Attention | Each token blends in context from all others (Section 4) |
| 6 | Feed-Forward Network (FFNN) | A small neural network refines each token’s vector |
Steps 5 and 6 (attention → feed-forward) form one “block,” and transformers stack this block N times (the “Nx” you see in architecture diagrams). Each layer builds richer understanding on top of the last.
💡 Why positional encoding is needed: RNNs got word order for free because they read left-to-right. Transformers process every word simultaneously — so without PE, “dog bites man” and “man bites dog” would look identical again (remember Post 3!). PE stamps each token with its position.
[DIAGRAM: the 6-step flow — tokenize → embed → +positional encoding → self-attention layer → FFNN layer → output]
7. Encoder vs Decoder — BERT, GPT & Translation
The original 2017 transformer had two halves, and modern models are built from one or both:
| Component | Job | Famous model |
|---|---|---|
| Encoder | Understand the input (reads the whole sentence bidirectionally) | BERT (Google) — great for classification, search |
| Decoder | Generate output one token at a time (predicts the next token) | GPT (OpenAI) — great for text generation |
| Encoder + Decoder | Transform one sequence into another | Machine translation (the original use case) |
Two training styles fall out of this split, both common interview topics:
- MLM (Masked Language Modelling) — used by BERT/encoders. Hide a word and predict it from both sides: “Ravi is a ▢ mentor” → predict “great”. Bidirectional understanding.
- Autoregressive / Next-token prediction — used by GPT/decoders. Given “Ravi is a”, predict the next word, then the next, left to right. This is literally how ChatGPT writes.
The decoder branch is where today’s LLM explosion lives — GPT, Llama, Mistral, DeepSeek, Qwen, Gemini, and the Claude family are all decoder-style, next-token generators built on this exact foundation.
[DIAGRAM: the classic transformer architecture with encoder (left) labelled BERT and decoder (right) labelled GPT]
8. LSTM vs Transformer — The Interview Table
This comparison comes up constantly in interviews. Memorize it:
| Feature | RNN / LSTM | Transformer |
|---|---|---|
| Processing style | Sequential (step by step) | Parallel (all tokens at once) |
| Speed | Slow | Fast |
| Long-term dependency | Limited (vanishing gradient) | Very strong |
| Architecture | Gates: input, forget, output | Attention-based (self-attention) |
The parallel-processing row is the one that changed the world. Being able to process “lots of data at once” is why context windows exploded — from a few hundred words in the LSTM era to 128K and even 1M+ tokens in modern LLMs. You can now feed a transformer an entire book.
9. The Modern LLM Family Tree
Everything you interact with today descends from that 2017 architecture. The lineage from the class notes:
NN → RNN/LSTM → Attention → Transformer (2017)
│
┌───────────────────┴───────────────────┐
ENCODER DECODER
(BERT) (GPT, Llama, Mistral,
understanding DeepSeek, Qwen, Gemini,
Claude family...)
→ text generationAnd the through-line for this whole series: a transformer’s very first step is still embedding — the same idea we built up from One-Hot Encoding through Word2Vec. Everything connects.
📌 One nuance worth knowing
Newer architectures (like Llama) tweak the original design — RMSNorm instead of LayerNorm, rotary positional encodings, grouped-query attention with KV-cache. The core — self-attention + feed-forward, stacked N times — is unchanged. Learn the fundamentals here and the variants are just footnotes.
Key Takeaways
✅ Transformers won by dropping the slow, sequential RNN/LSTM and keeping only attention, run fully in parallel — which is what let them scale to internet-sized data.
✅ Self-attention lets each word look at its neighbours and rebuild its meaning from context — finally solving “money bank” vs “river bank” with dynamic embeddings.
✅ Q, K, V (Query, Key, Value) are the engine: Query·Key scores relevance, and the weighted sum of Values becomes each word’s context-aware vector.
✅ The data flow is tokenize → embed → positional encoding → self-attention → feed-forward, stacked N times — with PE needed precisely because processing is parallel.
✅ Encoder (BERT, understanding) and decoder (GPT and the modern LLM family, generation) are the two halves — and every LLM you use is built from this exact blueprint.
What’s Next in the Series
| # | Post | Status |
|---|---|---|
| 7 | Word2Vec Hands-On: Custom vs Pretrained | ✅ Published |
| 8 | SOTA Embeddings: Sentence Transformers & APIs | ✅ Published |
| 9 | How Transformers Actually Work: Self-Attention | 📍 You are here |
| 10 | RAG — Retrieval Augmented Generation | ⬜ Coming next week |
You now understand embeddings (the retrieval half) AND transformers (the generation half). Next week we combine them into RAG — the technique that lets an LLM answer using your documents.
References
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Part of the GenAI from Scratch series — published every Friday at gradeupnow.in/genai-blog/