LLM Context Trimmer & Token Snoeier
Bespaar 20-50% op tokens door overbodige spaties, opmerkingen en opmaak te verwijderen.
LLM Context & Token Trimmer
Verklein promptcontext met 30%–60% door overbodige spaties, opmerkingen en oude conversaties te strippen.
=== SECTION 1: OVERVIEW & CORE PRINCIPLES === The platform provides a high-throughput event-driven microservices architecture. Every incoming request must carry a valid Bearer token in the Authorization header. ### Message Exchange Protocol: 1. Client sends POST request to /v1/events. 2. Gateway verifies HMAC signature. 3. Worker thread dispatches event to Kafka queue. User: How do I handle 429 Too Many Requests? Assistant: When receiving a 429 status, inspect the 'Retry-After' header and use exponential backoff with jitter. User: Can you show me an example? Assistant: Yes, here is an example in JavaScript using setTimeout.
Overzicht
Gratis online tool om prompt-context te optimaliseren en API-kosten te verlagen.
LLM Context Trimmer & Token Pruner Guide
While modern LLMs boast massive context windows, bloated context inflates API costs and degrades reasoning recall ("lost in the middle").
Source code files and markdown documentation often contain redundant whitespace, verbose comments, and badge links that consume expensive tokens needlessly.
This client-side utility prunes 20% to 50% of wasted tokens while preserving semantic intent.
How to Prune and Compress Prompt Context
Snel & IntuïtiefPaste Source Content
Paste your code, raw logs, or markdown documentation into the input panel.
Toggle Pruning Options
Enable comment stripping, whitespace collapsing, and badge removal.
Inspect Savings & Copy
Check the real-time token reduction percentage and copy the clean context.
Belangrijkste Functies & Voordelen
Geen uploads naar externe servers. Volledige controle over uw eigen data.
Snelle verwerking zonder wachtrijen of trage downloads.
Geen registratie, abonnement of verborgen limieten.
Naadloos te gebruiken op mobiel, tablet en desktop.
Betrouwbare W3C-conforme clientverwerking met hardwareversnelling.
- Strip SVG badges and image tags from GitHub READMEs before asking LLMs to summarize libraries.
- Collapse repetitive log timestamps to keep error traces within strict token limits.
Veelgestelde Vragen
2 Vragen & AntwoordenWill removing comments hurt code generation accuracy?
Usually not. Clean identifiers allow LLMs to comprehend logic without bulky license headers or inline comments.
How is token count estimated?
Using standard BPE tokenization approximations (~4 characters or 0.75 words per token).
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