Article 5 — How AI models learn: from embeddings to output

🧠 Recap

In Article 4, we unpacked Transformers — the architecture behind modern AI. Now let’s lift the hood and see how models actually learn, how weights and embeddings work, and how your input turns into a smart response.

⚙️ The Learning Process: Step-by-Step

AI models learn by training on large datasets. Here's how it happens:

1. Input Is Tokenized

Text is broken down into smaller chunks (tokens), like:

“I love AI” → [I, love, AI]

2. Tokens → Embeddings

Each token is mapped to a high-dimensional vector. These embeddings represent meaning — similar words have similar vectors.

3. Transformer Layers (The Brain)

Each layer in the model:

• Applies multi-head self-attention to understand relationships between tokens.

• Passes data through feedforward networks to transform it.

• Uses weights and biases that adjust during training to become more accurate.

4. Output Vector

The final layer produces output vectors, which are either:

• Converted into predictions (e.g., next word)

• Passed to a classifier (e.g., spam or not spam)

🧠 What Are Weights and Biases?

• Weights: Numbers that determine the strength of connections between neurons.

• Biases: Extra values that shift the output — they help the model learn more flexibly.

• Stored as arrays (tensors) in formats like .bin, .pt, .safetensors, etc.
  
  

During training, the model constantly tweaks these weights using gradient descent to reduce errors.

🔁 Backpropagation: How the Model Learns

1. Forward Pass: Input flows through the model → Prediction made.

2. Loss Calculation: How wrong was the prediction?

3. Backward Pass: Errors flow backward to adjust weights.

4. Repeat: Millions of times on vast data = smarter model.

📦 What’s Inside a Pretrained Model?

When you download a model like GPT or BERT, you're getting:

• Learned Weights: Trained over billions of words.

• Embeddings Matrix: Maps tokens to meaning.

• Model Architecture: The code that defines how layers and attention work.

Larger size = more layers, more neurons, more weights.

⚡ What Happens When You Prompt an AI?

1. Your Input: “Tell me a joke”

2. Tokenization & Embedding

3. Goes Through All Transformer Layers

4. Contextual Understanding Built Up

5. Next Word Predicted

6. Output Text Generated
   
   

The model doesn't store all training data. It stores learned patterns in its weights.

🧠 Why Embeddings Matter

• They’re the model’s mental map of meaning.

• Models use them to compare and relate ideas.

• Great embeddings = better understanding = smarter AI.

🧠 Summary: Core Concepts

📚 Coming Up in Article 6

We’ll now explore multimodal AI — how models like CLIP, DALL·E, and Sora understand not just text, but images, audio, and video.

🔑 Key Takeaway

AI learns by turning raw data into embeddings, processing it through transformer layers, adjusting weights, and improving through backpropagation. Every prompt you give travels this path to generate a meaningful output.