AI BriefingA 6-Step Guide to How LLMs Work Internally for Backend Developers
Key point
This explains the process by which an LLM receives a question and generates an answer, broken down into 6 steps.
Details
The process by which an LLM understands a user's question and generates an answer is explained in 6 steps: Tokenization, Embedding, Positional Encoding, Transformer & Attention, Prediction, and Decoding.
The first step, Tokenization, is the process of splitting the prompt into meaningful minimal units called Tokens and assigning a unique ID to each. Gemini models use SentencePiece, which is language-neutral and subword-based, allowing it to effectively handle languages like Korean where particles are attached to words.
The tokenizer operates in two clearly distinct phases: a Training phase, where it learns from large-scale data early in model development, and an Inference phase, where it applies already-established rules when processing a user's request.
The overall operating process is as follows:
- Tokenization: Splitting into tokens and mapping to IDs
- Embedding: Converting token IDs into vectors that carry meaning
- Positional Encoding: Adding word order information to the vectors
- Transformer & Attention: Understanding context and refining representations
- Prediction: Predicting the next token
- Loop & Decoding: Repeating the predicted tokens to complete the sentence
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