How to Write Prompts Like an OS: 7 Lessons from Claude’s Leaked System Prompt

Central Theme

The video analyzes the alleged leaked system prompt of a Claude model to extract advanced prompting techniques. The core message is a paradigm shift in prompt engineering: move away from simply instructing an AI what to do and towards creating a robust set of policies that defensively prevent failure modes. The prompt is treated less like an instruction and more like an operating system’s configuration file, with a heavy focus on what the model should not do.

Key Techniques Presented

The speaker identifies seven critical tactics from the prompt that can be applied by any user to improve output quality:

1. Instantiate Identity Upfront: Begin prompts by defining stable, concrete facts like the model’s identity, the current date, and its core capabilities. This establishes a firm context and reduces the model’s working memory load.
2. Triggers and Template Refusals: Implement explicit conditional logic (if X, then Y) to handle edge cases. Clearly defining boundaries and responses for specific scenarios prevents ambiguity and promotes consistent behavior.
3. Three-Tier Uncertainty Routing: Provide the model with a decision tree for handling information based on its timeliness: 1) Answer timeless information directly, 2) Answer slow-changing information but offer to verify, and 3) Search immediately for live information (e.g., today’s stock prices). This teaches the model when to act, not just how.
4. Lock Tool Grammar: When instructing a model on tool or API use, provide both correct and incorrect usage examples. Negative examples are powerful for teaching the model what to avoid, leading to more reliable tool integration.
5. Binary Style Rules: Use absolute, on/off rules instead of subjective guidelines. For example, “Never start with flattery” is much clearer and more effective than “Be concise,” as it leaves no room for interpretation.
6. Positional Reinforcement: In very long prompts, strategically repeat the most critical rules and constraints at different positions (e.g., every 500 tokens). This acts like a reminder and counteracts the degradation of the model’s attention over long contexts.
7. Post-Tool Reflection: Instruct the model to pause and output a “thinking” block after receiving data from a tool or API. This built-in cognitive checkpoint allows it to process the results and decide on a more accurate next step, especially in multi-step tasks.

Main Conclusions & Takeaways

• Think Like an OS Developer: Treat prompts not as magic incantations but as precise configuration files. Be specific, structured, and intentional.
• Embrace Defensive Prompting: Focus more passion and effort on defining what the model should not do. Exhaustively addressing potential failures (hallucinations, harmful content, etc.) leads to higher-quality results.
• Be Declarative, Not Just Imperative: Try to frame instructions as standing policies (“If X, always do Y”) rather than a simple sequence of commands. This creates more robust and predictable behavior.

Mentoring Question

Review one of your own complex prompts. What percentage of it is dedicated to what the AI should do versus what it should not do? How could you reframe some instructions as ‘defensive policies’ to prevent common failure modes?

Source: https://youtube.com/watch?v=74FvsJeljak&si=FEUpkB75ZWAXdrbQ