✂️ Prompt Length Trimmer
Auto-trim prompts to target character/token count without breaking words or sentences for optimal AI model input
Your Result:
1,847 → 1,200 characters (35% reduction)
Smart sentence trimming • Word boundaries preserved
📝 Trimmed Prompt (example)
📊 Trimming Statistics
Characters
Characters
Characters
How to Use This Prompt Length Trimmer
How to Use the Prompt Length Trimmer:
- Paste Your Text: Copy your long prompt into the text area. The tool handles any length of input text
- Set Target Limit: Enter your desired character or token count limit (10-50,000 range supported)
- Choose Limit Type: Select characters for exact counting or tokens for AI model estimation (~3-4 chars per token)
- Select Trimming Strategy: Choose how to trim - from end, middle, or smart removal of complete sentences/paragraphs
- Configure Options: Enable sentence structure preservation and detailed statistics as needed
- Trim Text: Click "Trim Prompt" to generate optimally sized output that preserves readability
- Copy Result: Use the copy button to get your trimmed text ready for AI models or character-limited applications
Pro Tips: Use token estimation for AI models (GPT, Claude), character counting for social media or SMS limits. Smart sentence/paragraph trimming preserves context better than simple truncation. The tool maintains word boundaries to prevent broken text that could confuse AI models.
How It Works
Advanced Text Trimming Technology:
Our trimmer uses sophisticated algorithms to reduce text length while preserving meaning and structure:
- Multi-Format Analysis: Simultaneously calculates exact character counts and estimates token usage using industry-standard ratios (3-4 characters per token) for accurate AI model planning
- Smart Boundary Detection: Implements natural language processing to identify sentence boundaries, paragraph breaks, and word boundaries using regex patterns and punctuation analysis
- Strategic Trimming Algorithms: Offers multiple trimming strategies including end truncation (fastest), middle removal (preserves intro/conclusion), and intelligent sentence removal (maintains context)
- Structure Preservation: Advanced word boundary protection ensures no broken words or incomplete sentences that could confuse AI models or reduce readability
- Token Estimation Engine: Uses statistical analysis based on OpenAI and Anthropic tokenization patterns to provide accurate token counts for optimal model input sizing
- Quality Optimization: Prioritizes preserving complete thoughts, maintaining paragraph structure, and ensuring coherent flow in the trimmed output
The system is optimized for AI prompt engineering, social media character limits, SMS constraints, and any application requiring precise text length control while maintaining professional quality and readability.
When You Might Need This
- • AI Prompt Engineering - Optimize long prompts for ChatGPT, Claude, and other models by trimming to token limits while preserving key instructions and context
- • Social Media Content - Trim posts, tweets, and captions to platform character limits (Twitter 280, LinkedIn 3000) without breaking sentences or hashtags
- • SMS and Text Messaging - Reduce long messages to standard SMS limits (160 characters) while maintaining clear communication and avoiding truncation
- • Email Subject Lines - Optimize email subjects to 50-60 character limits for better mobile display and higher open rates in marketing campaigns
- • Meta Descriptions - Trim SEO meta descriptions to Google's 150-160 character limit for optimal search result display and click-through rates
- • API Input Optimization - Prepare text inputs for APIs with strict character/token limits, ensuring requests don't exceed service quotas or fail
- • Database Field Limits - Trim content to fit database varchar limits and prevent data truncation errors during content management system operations
- • Translation Services - Prepare source text for translation APIs that charge by character or have input limits, optimizing costs and processing
- • Content Summarization - Create concise versions of long documents while preserving key points and maintaining readability for executive summaries
- • Documentation Limits - Trim technical documentation, code comments, and README files to platform-specific length requirements without losing clarity
Frequently Asked Questions
What's the difference between character and token counting?
Characters count every letter, space, and symbol exactly. Tokens are how AI models process text - roughly 3-4 characters per token, but varies by language and content. Use character counting for social media limits, token estimation for AI model inputs like GPT or Claude to avoid exceeding context windows.
How does the smart trimming preserve sentence structure?
Smart trimming analyzes punctuation patterns to identify complete sentences and paragraphs. Instead of cutting mid-sentence, it removes entire sentences or paragraphs while maintaining grammatical correctness. This prevents broken thoughts that could confuse AI models or readers, ensuring the trimmed text remains coherent and professional.
Which trimming strategy should I choose for AI prompts?
For AI prompts, use 'Smart Sentences' or 'Smart Paragraphs' to maintain context and instructions. 'Trim End' works for removing examples or less critical information at the end. 'Trim Middle' preserves your introduction and conclusion. Always enable 'Preserve sentence structure' for AI inputs to prevent parsing errors.
How accurate are the token estimates for different AI models?
Our estimates use industry averages: ~4 chars/token for GPT models, ~3 chars/token for conservative estimates. Actual tokenization varies by model and language - technical terms and non-English text may have different ratios. Use our estimates for planning, then verify with your specific model's tokenizer for critical applications.
Can this tool handle very long texts and special characters?
Yes, the tool handles up to 50,000 characters including Unicode, emojis, code snippets, and special formatting. It preserves markdown, HTML tags, and code blocks when possible. Large texts are processed efficiently using optimized algorithms. For massive documents, consider processing in sections for better performance.