Paste your prompt.
See what's wasting tokens.
Most AI prompts are 40–70% noise — politeness markers, filler phrases, indirect phrasing the model ignores. ContextStellar finds it and shows you exactly what to cut.
Runs entirely in your browser · No data sent anywhere · Free forever
How It Works
Three steps from verbose prompt to context-engineered precision
Paste Your Prompt
Drop any AI prompt into the editor. ContextStellar instantly analyzes it for context engineering antipatterns — politeness tokens, indirect phrasing, filler, weak intensifiers.
See Inline Suggestions
Every flagged issue shows exactly what to remove, how many tokens you save, and why it matters for LLM context windows. Accept individual suggestions or all at once.
Copy Optimized Context
One click copies your token-efficient prompt. Optionally apply AI Lingo mode to restructure with XML semantic tags, role assignment, and chain-of-thought triggers.
See the Difference
Real examples showing how context engineering principles transform verbose prompts into token-efficient signals
Code Analysis
Real-world example
Hi there! I would really appreciate it if you could please help me analyze this Python code very carefully and thoroughly check for any potential bugs, issues, or improvements that could be made. Thank you so much!
Analyze this Python code for bugs and improvements.
What was optimized:
Content Creation
Real-world example
I would like you to write a blog post about artificial intelligence. Please make it very informative and interesting. Feel free to include examples if you think they would be helpful. Thank you!
Write an informative blog post about artificial intelligence with examples.
What was optimized:
Data Analysis
Real-world example
Could you please analyze this dataset very carefully and provide a really detailed summary of the key insights? I'd appreciate if you could also explain the trends you notice. Thanks!
Analyze this dataset. Provide a detailed summary of key insights and trends.
What was optimized:
Context Engineering, Not Prompt Engineering
The real skill isn't writing better prompts—it's designing what information reaches the model, when, and in what format. Context is working memory. It's a finite resource with diminishing marginal returns.
“I really like the term ‘context engineering’ over prompt engineering. It describes the core skill better.”
— Tobi Lütke, CEO of Shopify
“Context engineering is in, and prompt engineering is out.”
— Gartner, July 2025
💡The Working Memory Problem
Research on “context rot” shows that as tokens increase, the model's ability to recall information decreases. Think of context like a desk versus a filing cabinet:
- ✓Desk (working memory): Limited space, instant access. Every item competes for attention.
- ✓Filing cabinet (long-term memory): Unlimited space, slower retrieval. Perfect for reference material.
The telegraph operators knew this 150 years ago: what earns a seat in working memory? Only signal. Never fluff.
Anthropic's Context Engineering Framework
According to Anthropic's engineering team, effective context engineering means designing systems that provide:
The Right Information
Only what's necessary. Strip politeness, filler, and redundancy.
At the Right Time
Context placement matters. Critical info goes early or late, not buried in the middle.
In the Right Format
Structured data beats prose. XML tags, JSON, or markdown—not verbose paragraphs.
Essential Context Engineering Resources
Anthropic: Effective Context Engineering for AI Agents
The authoritative engineering deep-dive — 6 components, production benchmarks.
Phil Schmid: The New Skill in AI is Not Prompting, It's Context Engineering
The definitive thought leadership piece that reframed the field.
Weaviate: Context Engineering — LLM Memory and Retrieval
How context engineering connects to RAG, retrieval, and memory systems.
LangChain: Context Engineering for Agents
Framework-level patterns for managing context in agentic workflows.
ArXiv: A Survey of Context Engineering for LLMs
Comprehensive academic taxonomy of context engineering techniques.
Why ContextStellar?
Apply context engineering principles to your daily AI workflow. No code, no setup.
Instant Context Analysis
Real-time antipattern detection as you type. No waiting, no submit button.
Reduce Token Costs
Context engineering cuts token usage 40–70% across all your AI prompts.
Better LLM Outputs
Cleaner context = clearer outputs. Less noise, more precision.
Learn Context Engineering
Understand why each suggestion improves your context window.
Works Everywhere
Mobile-first design. Copy-paste into Claude, GPT, Gemini, or any LLM.
AI Lingo Mode
Transform prompts using XML structure and cognitive mode framing — Anthropic best practices.
Frequently Asked Questions
Everything you need to know about context engineering and ContextStellar
What is context engineering?
Context engineering is the discipline of designing what information reaches an LLM, when, and in what format. As Tobi Lütke (Shopify CEO) put it: "I really like the term context engineering over prompt engineering — it describes the core skill better." Unlike prompt engineering (just crafting better sentences), context engineering manages the entire context window — instructions, memory, retrieved data, conversation state, and format.
How is ContextStellar different from prompt optimizers?
Most prompt optimizers focus on making sentences sound better. ContextStellar applies context engineering principles: we analyze signal-to-noise ratio, context rot, and context pollution across your entire prompt. We also offer AI Lingo mode, which restructures prompts using Anthropic's recommended XML semantic boundaries, cognitive mode framing, and chain-of-thought triggers.
How much can I reduce my token costs?
Context engineering best practices consistently achieve 40–70% token reduction for typical prompts. Anthropic's own research on agent context engineering shows up to 84% token reduction in production systems. ContextStellar focuses on the human-written portion: removing politeness (1–2 tokens each), indirect phrasing (3–6 tokens), weak intensifiers (1 token), filler phrases (2–3 tokens), and redundant references (2–3 tokens).
What is "context rot" and why does it matter?
Context rot is the phenomenon where LLM performance degrades as context fills up — even within the technical context window limit. Research shows models struggle to recall information from the middle of long contexts (the "lost in the middle" problem). Every token of filler you add pushes critical information further from optimal positions and reduces the model's effective reasoning capacity.
What is AI Lingo mode?
AI Lingo transforms prompts using linguistic patterns that resonate with transformer models: XML role assignment (<role>Expert analyst</role>), cognitive mode framing (<mode>systematic-analytical</mode>), output format specification, and chain-of-thought triggers. These patterns are based on Anthropic's context engineering best practices and activate specialized reasoning pathways in LLMs.
Is there a free plan?
Yes — ContextStellar is completely free with no account required. All context analysis, token counting, and AI Lingo transformations run locally in your browser. No data is sent to any server.
Ready to Apply Context Engineering?
Reduce token costs, improve LLM outputs, and master context window management today. Free. No account required.
Get Started Free