Special Tokens

Special tokens are reserved entries in a tokenizer vocabulary that carry structural or semantic meaning rather than representing ordinary text content. Common special tokens include the beginning-of-sequence marker (BOS), end-of-sequence marker (EOS), padding token (PAD), unknown-input token (UNK), separator (SEP), classification (CLS), and various role markers used in chat-tuned models. Modern chat-tuned LLMs use elaborate sets of special tokens to demarcate system prompts, user messages, assistant responses, and tool calls — for example, OpenAI's chat models use markers like <|im_start|> and <|im_end|>, and Llama 3 uses <|begin_of_text|>, <|start_header_id|>, and similar. Misusing or omitting required special tokens can dramatically degrade model performance, which is why most production deployments use the official chat templates provided by model vendors. AI governance teams document special token usage in prompt templates because changes to template structure between model versions can silently break compliance-critical workflows. Some special tokens are also used for tool calling, function output formatting, and prefix tuning.

Special-token handling across Centralpoint models: Centralpoint's Prompt Manager handles per-model special tokens transparently — system markers, role headers, tool call delimiters — so administrators don't need to memorize each provider's template. Prompts stay local, tokens are metered uniformly, and chatbots embed through one line of JavaScript across portals.


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