2 links tagged with all of: system prompts + prompt engineering
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A team member at Anthropic shared the exact LOOPS.md file Andrej Karpathy uses. When loaded into Claude, it shifted the model from generic replies to responses tailored to the user’s thinking. The approach highlights building a system prompt layer rather than chatting directly with the model.
- A supposed Anthropic teammate shared Karpathy's "LOOPS.md" file, claimed to be his personal prompt-engineering setup for Claude.
- The pitch is that using it shifts Claude from generic answers to step-by-step responses matching the user's own thinking style.
- The core concept: treat Claude as wrapped in a system layer (templates, token budgets, chain-of-thought triggers, multi-pass refinement protocols) rather than prompting it directly.
- Framed as urgent/scarce content ("save it before it disappears"), a hallmark of unverifiable social-media hype rather than a sourced claim.
This article examines how Claude Code constructs its system prompts by analyzing recently leaked source code. It highlights the dynamic nature of these prompts, which are built from various components based on conditional statements. The author also outlines additional elements that contribute to context, like tool definitions and conversation history.
- Claude Code's system prompt isn't static text—it's dynamically assembled at runtime from conditional components, revealed by a recent source code leak.
- The prompt mixes always-included elements (like core instructions) with conditional ones (like tool-specific guidance) that only appear based on context.
- Beyond the system prompt itself, the full context sent to the model includes tool definitions, conversation history, attachments, and skills—all engineered together to shape responses.