Tools: How Transformers Work Inside an LLM (Step by Step)

1️⃣ Big Picture: Where Do Transformers Fit in an LLM? ## 2️⃣ What Happens First When Input Enters the Model? ## Example Input ## 🔹 Step 1: Tokenization ## 🔹 Step 2: Embedding ## 🔹 Step 3: Positional Encoding (Very Important) ## 3️⃣ What’s Inside a Transformer Block? ## 4️⃣ Self-Attention: The Core Power of Transformers 🧠 ## Example ## 5️⃣ How Attention Works Internally (Q, K, V) ## 6️⃣ Why Multi-Head Attention? ## 7️⃣ Masked Self-Attention (Critical for GPT Models) ## 8️⃣ Feed Forward Network: Pattern Builder ## 9️⃣ Residual Connections & Layer Normalization ## 🔟 What Happens After Multiple Transformer Blocks? ## 1️⃣1️⃣ How Is the Next Token Chosen? ## 1️⃣2️⃣ One-Line Summary ## 1️⃣3️⃣ Why Were Transformers Necessary for LLMs? The full LLM pipeline looks like this: 👉 The Transformer is the brain of the LLM 👉 All context understanding and relationship modeling happens here LLMs don’t process words—they process tokens. Each token is converted into a numerical vector: This is a mathematical representation of meaning. Transformers do not understand word order by default. So position information is added to embeddings. Without positional encoding, these would look identical ❌ A single Transformer block has two main components: LLMs contain many such blocks: Each block refines the representation further. Self-attention means: To understand one token, the model determines which other tokens are relevant. The token “he” attends to “Rahim”. This is how Transformers learn context and relationships. Each token is transformed into three vectors: The core calculation: Tokens with higher scores contribute more strongly. One attention mechanism isn’t enough. Different heads focus on different aspects: This makes Transformers extremely powerful. GPT-style models cannot see future tokens. The token “Today” cannot attend to future tokens. 👉 This is enforced using masked self-attention 👉 The model only looks at past tokens That’s why LLMs generate text step by step. Attention finds relationships. The feed forward network: But at massive scale. Deep Transformers can become unstable. Without these, modern LLMs wouldn’t work. After passing through many blocks, tokens become: Final output flows through: Softmax produces probabilities: The selected token becomes the next output, and the process repeats 🔁 Transformers process all tokens together, use attention to understand relationships, and leverage that context to predict the next token. Because Transformers provide: ✅ Long-range context understanding ✅ Parallel computation ✅ Strong attention-based modeling ✅ Massive scalability No previous architecture offered all of these together. Follow me on : Github Linkedin Threads Youtube Channel Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: Input Text ↓ Tokenizer ↓ Embedding + Positional Encoding ↓ 🔥 Transformer Blocks (Core Brain) ↓ Softmax (Probability) ↓ Next Token Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Input Text ↓ Tokenizer ↓ Embedding + Positional Encoding ↓ 🔥 Transformer Blocks (Core Brain) ↓ Softmax (Probability) ↓ Next Token CODE_BLOCK: Input Text ↓ Tokenizer ↓ Embedding + Positional Encoding ↓ 🔥 Transformer Blocks (Core Brain) ↓ Softmax (Probability) ↓ Next Token CODE_BLOCK: "Today the server is down" Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: "Today the server is down" CODE_BLOCK: "Today the server is down" CODE_BLOCK: ["Today", "the", "server", "is", "down"] Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: ["Today", "the", "server", "is", "down"] CODE_BLOCK: ["Today", "the", "server", "is", "down"] CODE_BLOCK: "server" → [0.32, -1.10, 0.87, ...] Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: "server" → [0.32, -1.10, 0.87, ...] CODE_BLOCK: "server" → [0.32, -1.10, 0.87, ...] CODE_BLOCK: Today the server is down The server is down today Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Today the server is down The server is down today CODE_BLOCK: Today the server is down The server is down today CODE_BLOCK: [ Multi-Head Self-Attention ] ↓ [ Feed Forward Neural Network ] Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: [ Multi-Head Self-Attention ] ↓ [ Feed Forward Neural Network ] CODE_BLOCK: [ Multi-Head Self-Attention ] ↓ [ Feed Forward Neural Network ] CODE_BLOCK: Rahim fixed the server because he understands debugging. Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Rahim fixed the server because he understands debugging. CODE_BLOCK: Rahim fixed the server because he understands debugging. CODE_BLOCK: Attention Score = Q · K Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Attention Score = Q · K CODE_BLOCK: Attention Score = Q · K CODE_BLOCK: Output = weighted sum of V Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Output = weighted sum of V CODE_BLOCK: Output = weighted sum of V CODE_BLOCK: Multi-head attention = multiple perspectives Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Multi-head attention = multiple perspectives CODE_BLOCK: Multi-head attention = multiple perspectives CODE_BLOCK: Today the server ___ Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Today the server ___ CODE_BLOCK: Today the server ___ CODE_BLOCK: Linear → Activation → Linear Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Linear → Activation → Linear CODE_BLOCK: Linear → Activation → Linear CODE_BLOCK: "server" understands the full sentence context Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: "server" understands the full sentence context CODE_BLOCK: "server" understands the full sentence context CODE_BLOCK: Linear Layer ↓ Softmax Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Linear Layer ↓ Softmax CODE_BLOCK: Linear Layer ↓ Softmax CODE_BLOCK: down → 55% slow → 30% offline → 10% Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: down → 55% slow → 30% offline → 10% CODE_BLOCK: down → 55% slow → 30% offline → 10% - Small models → 12–24 blocks - Large models → 48–96+ blocks - Query (Q): What am I looking for? - Key (K): What information do I contain? - Value (V): What content should be passed forward? - Subject relationships - Time / tense - Cause–effect - Learns abstractions - Builds patterns - Extracts deeper meaning - Residual connections preserve information - Layer normalization stabilizes training - More context-aware - More meaningful - More informed