Split large text, datasets, transcripts, and code by custom delimiters, character limits, word counts, or lines. Features smart word boundaries and AI prompt chunking with sliding overlap.
In computer science and data engineering, string splitting is the process of breaking a contiguous sequence of characters into a structured array of substrings based on predefined boundary rules.
While a basic split function simply breaks text wherever it encounters a single delimiter (such as a comma or pipe), advanced text chunking requires handling boundary edge cases:
Prevents slicing words across chunks by looking backward to the nearest whitespace or punctuation mark.
Repeats a defined number of characters between adjacent chunks to maintain context continuity for AI prompts.
Automatically strips trailing whitespace, eliminates empty segments, and removes duplicates.
Large Language Models (like ChatGPT, Claude, and Gemini) have fixed input token windows. When attempting to summarize 50-page PDFs, book manuscripts, or long meeting transcripts, passing the entire raw text at once causes hallucinations, lost details, or hard token errors.
Splitting long text into segments of 2,000–4,000 characters ensures each prompt fits comfortably within token quotas, allowing deep semantic analysis without truncation.
Adding a 150-character sliding overlap between chunks ensures that sentences crossing a chunk boundary remain coherent across separate prompts.
Paste your raw document, CSV row, list, or code into the left text box.
Select from the top tabs: By Delimiter (comma, pipe, semicolon, custom), By Character Length, By Word Count, By Line Count, or AI Chunker.
Fine-tune chunk boundaries, enable Smart Word Boundary, or configure sliding character overlap.
Copy individual chunk cards with one click, copy all parts rejoined with newlines, or download as a text file.
Convert horizontal comma-separated or pipe-delimited data strings into vertical line-by-line lists for Excel and Google Sheets.
Segment long marketing copy or newsletter announcements into chunks $\le 160$ characters for SMS or 280 characters for Twitter (X) threads.
Split raw podcast or video transcripts every 5 lines or 50 words to create readable SRT subtitles and YouTube video descriptions.
Divide massive server log dumps or JSON data every 100 lines for faster bug investigation and team review.
An online text splitter is a utility that breaks long bodies of text, datasets, transcripts, or code into smaller, manageable chunks based on specified rules—such as custom delimiters (commas, pipes, slashes), fixed character limits, word counts, or line counts.
When splitting text strictly by character count, standard algorithms often slice words directly down the middle (e.g. splitting 'computation' into 'comput' and 'ation'). Enabling 'Smart Word Boundary' instructs the splitter to search backward for the nearest space, punctuation mark, or newline before the character limit, ensuring every word remains intact.
Large Language Models (like ChatGPT, Claude, and Gemini) have context window constraints and input token limits. When summarizing massive documents, books, or interview transcripts, chunking the content into bite-sized segments (e.g., 2,000–4,000 characters) allows the AI to process each section accurately without exceeding token limits or losing conversational context.
Chunk overlap repeats a small portion of text (e.g., 100–200 characters) at the beginning of each consecutive chunk. This ensures that thoughts or sentences that bridge across a chunk boundary are not lost, providing critical context continuity for LLM processing and Retrieval-Augmented Generation (RAG) pipelines.
Yes! Select 'By Delimiter', choose Comma (,) or Pipe (|), and set the output rejoining mode to 'Newlines'. The tool will instantly convert your horizontal comma-separated list into a clean, vertical line-by-line list.
Yes. 100% of the text segmentation, chunking, and string transformations execute locally inside your browser using JavaScript. None of your data is sent to external servers or stored in any database.
