What Is A Token In AI? [Explained]

By 2025, it's anticipated that 30% of outbound marketing messages from large organizations will be synthetically generated, highlighting the importance of tokenization in AI discussions. Tokens, the smallest units of text processed by AI models, are crucial in natural language processing (NLP) tasks as they convert text into numerical vectors for mathematical manipulation. Different tokenization methods, such as word-based, character-based, subword, n-gram, and sentence tokenization, each have specific strengths suited to various NLP tasks. Byte-Pair Encoding (BPE) is a notable method that balances character-level and word-level tokenization, effectively handling out-of-vocabulary words by breaking them into subword units. Tokens impact model accuracy, processing speed, cost, and multilingual support, making efficient tokenization essential for AI's understanding and generation of language. The NLP market is projected to grow significantly, driven by tokenization advancements. Token count influences processing time and costs, with AI services often pricing based on token usage. Platforms like Voiceflow optimize token usage for cost-effective AI agent deployment, offering businesses a way to leverage advanced tokenization for enhanced customer support experiences.