The basic unit of text processed by language models—typically a word, subword, or character. Tokenizers (BPE, SentencePiece) split text into tokens for model input. 'Solana blockchain' might tokenize as ['Sol', 'ana', ' block', 'chain']. Token count determines context window usage and API billing. Not to be confused with blockchain tokens (cryptocurrency assets).
Start with the shortest useful explanation before going deeper.
The basic unit of text processed by language models—typically a word, subword, or character. Tokenizers (BPE, SentencePiece) split text into tokens for model input. 'Solana blockchain' might tokenize as ['Sol', 'ana', ' block', 'chain']. Token count determines context window usage and API billing. Not to be confused with blockchain tokens (cryptocurrency assets).
Use the quick analogy first so the term is easier to reason about when you meet it in code, docs, or prompts.
Think of it as a piece of the context or inference stack behind agentic and LLM-powered Solana products.
Place the term inside its Solana layer so the definition is easier to reason about.
LLMs, RAG, embeddings, inference, and agent-facing primitives.
Turn the term from vocabulary into something operational for product and engineering work.
This term unlocks adjacent concepts quickly, so it works best when you treat it as a junction instead of an isolated definition.
Use this compact block when you want to give an agent or assistant grounded context without dumping the entire page.
Token (AI/NLP) (token-ai) Category: AI / ML Definition: The basic unit of text processed by language models—typically a word, subword, or character. Tokenizers (BPE, SentencePiece) split text into tokens for model input. 'Solana blockchain' might tokenize as ['Sol', 'ana', ' block', 'chain']. Token count determines context window usage and API billing. Not to be confused with blockchain tokens (cryptocurrency assets). Aliases: AI Token Related: LLM (Large Language Model), Context Window
Use glossary context, relationships, mental models, and builder paths to get structured answers instead of generic chat output.
Optional: paste Anchor, Solana, or Rust code so the Copilot can map primitives back to glossary terms.
The Copilot will answer using the current term, related concepts, mental models, and the surrounding glossary graph.
These branches show which concepts this term touches directly and what sits one layer beyond them.
A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.
The maximum amount of text (measured in tokens) an LLM can process in a single interaction. Larger windows enable processing more code/documentation at once. Sizes vary: GPT-4 (128K tokens), Claude (200K tokens), Gemini (1M+ tokens). One token ≈ 4 characters in English. Context window limits affect how much codebase an AI can analyze in a single request.
These are the next concepts worth opening if you want this term to make more sense inside a real Solana workflow.
Glossary entries become useful when they are connected. These links are the shortest path to adjacent ideas.
A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.
The maximum amount of text (measured in tokens) an LLM can process in a single interaction. Larger windows enable processing more code/documentation at once. Sizes vary: GPT-4 (128K tokens), Claude (200K tokens), Gemini (1M+ tokens). One token ≈ 4 characters in English. Context window limits affect how much codebase an AI can analyze in a single request.
These entries live beside the current term and help the page feel like part of a larger knowledge graph instead of a dead end.
A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.
The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).
A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.
A large AI model trained on broad data that can be adapted for many downstream tasks. Foundation models (GPT-4, Claude, Llama 3, Gemini) are pre-trained on internet-scale text/code and can be fine-tuned, prompted, or used via APIs for specific applications. The term emphasizes that one base model serves as the foundation for diverse use cases rather than training task-specific models.