Prompt Trimmer
AI prompt trimmer online. Trim text to fit token limits. Sentence-aware and paragraph-aware modes—reduce prompt length free.
Quick presets:
Input Text
0 tokens✂️
Enter text above to trim
Trim Modes
Hard Cut
Cuts exactly at token boundary. May break mid-word.
Sentence-Aware
Keeps complete sentences. Cleaner output.
Paragraph-Aware
Keeps complete paragraphs. Best for structured text.
Features
- Precision token-based truncation to guarantee your prompt fits within any LLM context limit
- Semantic boundary awareness: trim text safely at the nearest sentence or paragraph boundary
- Directional preservation: choose to keep the beginning (for instructions) or the end (for chat history)
- Real-time comparative metrics showing exact token reduction and byte-size savings
- Support for OpenAI tiktoken encodings to prevent accidental sub-word fragmentation
- One-click clipboard export of the perfectly sized, model-ready string
Common Use Cases
- Safely truncating massive retrieved documents in RAG architectures to fit exactly into the prompt window
- Automating the eviction of old messages in a conversational AI chatbot to prevent HTTP 400 Context Length errors
- Cleaning massive datasets for fine-tuning by aggressively standardizing the token length of all training examples
- Preparing concise, context-dense snippets for search engine indexing or metadata generation
- Preventing hallucinations caused by cutting off sentences mid-word when feeding data to an LLM
The Danger of Blind Truncation
Truncating text blindly by a static character count (e.g., text.substring(0, 4000)) is a major anti-pattern in AI engineering. It frequently slices words in half or leaves dangling punctuation, which severely degrades the LLM's comprehension and often leads to hallucinations or formatting errors in the output.
Smart Trimming Strategies:
- Exact Token Trimming: Slices the string precisely at the token boundary (e.g., exactly 8,192 tokens). This is the most space-efficient method but may leave an incomplete sentence at the end.
- Sentence-Aware Trimming: Calculates the token limit, but then intentionally walks backward to find the nearest sentence-ending punctuation (like a period or exclamation mark). This sacrifices a few tokens of space but guarantees the model reads a complete, coherent thought.
- Directional Preservation: When sending chat history, you want to trim from the top down (Preserve End), dropping the oldest messages. When sending a strict instructional prompt, you want to trim from the bottom up (Preserve Start) to ensure the system instructions are never lost.
Examples
Invalid - Blind Character Trim (Bad)
Original: "The financial report states we made $4,000,000."
Trimmed: "The financial report states we made $4,00"
Result: The AI assumes you made $400. Valid - Sentence-Aware Trim (Good)
Original: "The company is growing. The financial report states we made $4,000,000."
Trimmed: "The company is growing."
Result: The AI receives less data, but the data is completely accurate.Frequently Asked Questions
Will Exact Token trimming cut off in the middle of a word?
Because token boundaries are respected, it will not cut off in the middle of a "token". However, because complex words are made of multiple sub-tokens, it is entirely possible for it to cut off in the middle of a multisyllabic word. For production data, always use Sentence-Aware trimming.
Why should I "Preserve End" for chat memory?
In conversational AI, the most recent message (the end of the string) is the most vital context for the model to generate a relevant reply. The oldest messages (the start of the string) are the least relevant and should be the first things truncated when you run out of space.
Is it better to trim the text or summarize it?
If you have the compute budget, passing the overflowing text through a cheaper, faster model (like Claude 3 Haiku or GPT-4o-mini) to summarize it is usually superior to outright trimming. Trimming is best used as an absolute failsafe to prevent API crashes.
💡 Tips
- Always use "Sentence Aware" mode when feeding documents into a Retrieval-Augmented Generation (RAG) pipeline to prevent providing partial, confusing facts to the LLM.
- Use "Exact Token" mode only when you need to squeeze in absolutely every possible bit of information and don't care about narrative flow (like raw log files).