Token decoding in the context of LLMs (like Claude or ChatGPT) refers to the process of converting model outputs back into human-readable text.

How it works:

1. Tokenization - Text is split into tokens (subword units, ~4 characters average)

   • "Postmoderne" → ["Post", "mod", "erne"]
   • Numbers, special chars get their own tokens

2. Decoding strategies:

   • Greedy - Always pick highest probability token
   • Beam search - Track multiple candidate sequences
   • Sampling - Randomly select from probability distribution
   • Top-k - Sample from top k most likely tokens
   • Top-p (nucleus) - Sample from smallest set summing to probability p
   • Temperature - Controls randomness (lower = more deterministic)
3. Output - Token IDs → vocabulary lookup → text string

Example in AI SDK:

Would you like me to:

1. Add a token visualization to the app showing how text breaks into tokens
2. Integrate Claude API for AI-powered insights in the Ikigai dashboard
3. Something else related to token/decoding concepts

I'll add a token visualization component to the app that shows how text breaks into tokens, fitting the contemplative analytical aesthetic.

I've added a Token Visualizer component to the Belas Ikigai dashboard. The component displays an interactive tokenization visualization with a text input area, color-coded token display, optional token IDs, and statistics showing character count, token count, and the ratio between them. It's styled to match the contemplative deep navy aesthetic with German labels ("Token Dekodierung", "Sprachfunktion", etc.) and references Claude Anthropic as the decoding source.