AI Text Splitter

The AI Text Splitter ingests a single long text and returns an array of chunks sized for AI models with token limits. It offers adjustable chunk_size_tokens, optional overlap_tokens, and sentence-aware splitting to preserve meaning. The tool is designed for developers integrating prompts, data scientists preparing datasets, and content teams handling large documents. Conceptually, it segments content into logically bounded units that can be processed sequentially without losing context. Core value lies in predictable chunk boundaries, optional preservation of sentence structure, and an export-ready JSON structure for downstream pipelines. Users feed the input_text and configure how aggressively the content is divided. The system estimates token usage per chunk and can operate in language-specific modes when a language_hint is provided, improving token budgeting for multilingual content. Unique advantages include flexible overlap to maintain context across chunks and a simple heuristic token estimator that works without requiring model-specific tokenizers. Typical use cases include preparing large PDFs for model summarization, batching news articles for sentiment analysis, and segmenting long guidelines for rule-based processing. The tool emphasizes deterministic results, enabling reproducible experiments and consistent data pipelines while avoiding reliance on proprietary tokenizers. It is suitable for technical teams, content engineers, and researchers seeking a lightweight yet rigorous pre-processing step for AI workflows.