AI Context Engineering Tool
Systematically design the context you feed an LLM — split system prompts, arrange references and history, estimate token usage live and compress it for stabler, cheaper output
Interactive tool will be available soon
Meanwhile, read the guide below to understand how it works
Features
- ✓ Modularly arrange system prompts, references and conversation history
- ✓ Live token accounting per block against your total budget
- ✓ Automatic compression and summarization that keeps the essentials
- ✓ Template library for support, coding, writing and RAG scenarios
- ✓ Version diffing to see how context changes affect output
How to Use
- Pick a scenario template or start from a blank canvas
- Fill in the system prompt, references and message history separately
- Review token usage and compress whatever exceeds the budget
- Preview the assembled context and export it to your code or API
FAQ
What is an AI Context Engineering Tool?
A tool to design and manage the context you feed a model. It offers modular arrangement, live token accounting, auto-compression, templates and version diffing. Input: system prompt, references and history. Output: a ready-to-call context string. Built for AI developers and prompt engineers.
What is context engineering?
It means systematically designing everything sent into the model — prompts, retrieved material, history and tool definitions — rather than only polishing a single prompt. It directly affects output stability and cost.
How does it differ from prompt optimization?
Prompt optimization is about wording; context engineering is about the overall information structure and budget — what goes in, what gets summarized and where each part sits.
Are the token counts accurate?
Counts are estimated from each model's tokenizer and may differ slightly from actual billing, which is accurate enough for budgeting and trade-offs.
Which models are supported?
All major vendors' models are supported; tokenization and context windows differ, so you can switch models in the tool to compare.
Is it free?
Basic arrangement and token counting are usually free, while team collaboration, version history and bulk testing are paid features.