What happens when an AI model receives input?

The internal execution journey of an AI query: text tokenization, tensor embedding, multi-layer forward propagation matrix multiplications, non-linear activations, and output decoding.

Real-World Mental Model

“Translating English to Morse code ➔ routing pulses through telephone switches ➔ decoding pulses back into audible speech.”

INTERACTIVE ENGINE: INPUT FLOW

Live Reactive

Step 01: String Tokenization (BPE)

Active State

Raw UTF-8 text string is parsed by Byte-Pair Encoding into token discrete integer IDs.

Tensor Intermediate Payload:

"AI" -> [15340], " Engineering" -> [8923]

CORE PRINCIPLES

What You Must Understand

1. Tokenization: Text converted to numeric token ID sequence.

2. Embedding Lookup: Token IDs mapped to high-dimensional continuous vectors.

3. Forward Pass: Layer-by-layer matrix multiplication and attention calculations.

4. Decoding: Softmax logit sampling converts top tokens back to readable text string.

Reference Implementation

// Input Pipeline
User Prompt -> Tokenizer -> Embedding Tensor -> Transformer Layers -> Logits -> Detokenize -> Stream

Production Failure Mode

Overlooking tokenization artifacts (e.g. numbers or trailing whitespace behaving differently across tokenizers).

View Full 26-Topic Curriculum Table

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What You Must Understand

1. Tokenization: Text converted to numeric token ID sequence.

2. Embedding Lookup: Token IDs mapped to high-dimensional continuous vectors.

3. Forward Pass: Layer-by-layer matrix multiplication and attention calculations.

4. Decoding: Softmax logit sampling converts top tokens back to readable text string.