Prompt Optimizer — Trim Filler, Cut Tokens, Keep Meaning
Paste a raw prompt and get a tightened version — filler words and redundant instructions stripped, token count reduced, meaning preserved. Free, runs entirely in your browser.
Token Estimate by Model
Filler Words Detected (highlighted)
Restructured Prompt (5-Section Framework)
Optimization Suggestions
What is Prompt Optimization?
LLMs are extremely sensitive to prompt quality. The same task, written differently, can yield 10× the result. Prompt optimization covers: (1) Structuring — splitting messy descriptions into Role / Task / Context / Examples / Output format; (2) Trimming — removing filler words; (3) Constraining — adding output format / length / forbidden topics; (4) Anchoring — pinning with 1-2 input/output examples.
Why use dibi8 Prompt Optimizer?
- 🔒 Fully client-side: all processing in your browser, your prompt never leaves the device, zero logging
- 🌍 Native 4-language: separate filler dictionaries for English / Chinese / Korean / Vietnamese
- 📊 Accurate token estimates: uses OpenAI's official tokenizer (4 models compared: GPT-4o / Claude Sonnet / Gemini / DeepSeek V4)
- ⚡ Real-time feedback: analyzes as you type with 280ms debounce
The 5-Section Framework (Role / Task / Context / Constraints / Examples / Output)
This is the mainstream prompt engineering framework recommended by both Anthropic and OpenAI. Role sets identity, Task states the goal, Context provides background, Constraints limit scope, Examples anchor output, Output Format standardizes shape.