How ChatGPT Work Step by Step – day 70

Understanding How ChatGPT Processes Input: A Step-by-Step Guide Understanding How ChatGPT Processes Input: A Step-by-Step Guide Introduction ChatGPT is a language model based on the Transformer architecture. It generates responses by processing input text through several neural network layers. By understanding each step, we can appreciate how ChatGPT generates coherent and contextually appropriate replies. Additionally, ChatGPT follows a decoder-only approach (as in the GPT family of models). This means it uses a single stack of Transformer layers to handle both the input context and the generation of output tokens, rather than having separate encoder and decoder components. Step 1: Input Tokenization What Happens? The input text is broken down into smaller units called tokens. ChatGPT uses a tokenizer based on Byte Pair Encoding (BPE). Neural Network Involvement: No — Tokenization is a preprocessing step, not part of the neural network. Example: Input Text: “Hi” Tokenization Process: Text Token ID “Hi” 2 Figure 1: Tokenization Input Text: “Hi” ↓ Tokenization ↓ Token IDs: [2] Step 2: Token Embedding What Happens? Each token ID is mapped to a token embedding vector using an embedding matrix. The embedding represents the semantic meaning of the token. Neural Network Involvement: Yes — This is part…