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TabBench

Prompt & Context Cleaner

Sanitize prompts, mask PII (emails, cards, IPs), strip HTML and comments, and optimize context sizes.

Runs in your browser. Nothing you add is uploaded.

What the Prompt & Context Cleaner does

Raw text copied from web scrapes, documentation, and code repositories is packed with markdown syntax, HTML tags, line comments, and excessive whitespace that waste context tokens and inflate API costs. Worse, pasting logs can accidentally leak sensitive PII, emails, or AWS credentials. This tool cleans markup, collapses whitespace, and masks PII locally before you send prompts to LLMs.

How to clean and sanitize prompts

  1. Paste your raw document, code snippet, log file, or web scrape into the input box.
  2. Toggle cleaning options: Strip HTML, Strip Comments, Strip Markdown, and Collapse Blank Lines.
  3. Enable Mask PII to automatically redact email addresses, phone numbers, IP addresses, and AWS access keys.
  4. Review the Savings & Efficiency stats to see exact tokens and characters saved.
  5. Copy the sanitized, optimized context directly for your ChatGPT prompt or RAG vector pipeline.

The Prompt & Context Cleaner runs entirely in your browser — nothing you enter is uploaded, stored, or logged.

When to use it

RAG Document Chunk Pre-Processing

Clean raw HTML and documentation before generating vector embeddings, maximizing semantic density and reducing vector database storage costs.

Redacting Sensitive Data and Secrets

Protect customer privacy and infrastructure secrets by masking emails, phone numbers, server IPs, and AWS keys before sending logs to cloud LLMs.

Optimizing Long Code Contexts

Strip bulky comment blocks and licensing boilerplate from source code files before asking an AI model to refactor or audit functions.

Good to know

  • Stripping comments and empty lines can reduce code context payload sizes by up to 30-40%.
  • PII redactions replace sensitive data with clean markers like [EMAIL_REDACTED] that LLMs understand seamlessly.
  • All processing happens client-side using JavaScript regex: zero data is uploaded to any server.

Frequently asked questions

Why should I clean context before sending it to an LLM?

Uncleaned web pages and code files contain comments, markup, and blank lines that waste context window capacity and drive up API token costs. Cleaning also protects sensitive PII and API keys from leaking into LLM logs.

Does this tool work completely offline?

Yes. All sanitization, regex replacements, and PII redactions happen locally in your browser. No sensitive documents or logs are ever transmitted across the network.

Does stripping markdown hurt LLM comprehension?

For pure data retrieval and context lookup tasks, stripping decorative markdown (bolding, headers, list formatting) reduces token counts without affecting the semantic meaning.

What PII formats are recognized?

The redaction engine detects standard email addresses, international phone numbers, IPv4 addresses, major credit card patterns, and AWS access key IDs.

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