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**LLMs are brilliant, yet mindless pattern matchers.** Because they lack properties similar to mental models, they can't imagine how the correct output should look like before their context window is filled with files.
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Coding agents (Cursor, Claude Code, etc.) address this limitation via technique called tool calling. The system starts each task from a clean slate, by analyzing your instructions it navigates the codebase to include all necessary files in the context window. While going all-in with natural language interface for programming may sound appealing, these systems don't offer short feedback loops critial in day-to-day work, or cost-efficiency allowing sustainable mass adoption.
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Because contents of the context window determine output quality, Code Web Chat is designed in a way **the model is guided with known set of files**, enabling simple, task-focused instructions and complete edits in seconds!
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With files sorted by update recency, CWC helps utilize **[prompt caching](https://platform.openai.com/docs/guides/prompt-caching) across tasks**—consistently **save up to 90% on input tokens**, while spending the more expensive output tokens directly on edits you're looking for.
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**Example message:**
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```
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<system>
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Whenever showing a new, updated, renamed, or deleted file, provide a brief explanation...
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