Overview
Using the asgeirtj/system_prompts_leaks archive, this series explains how captured system prompts turn model capability into product contracts for roles, tools, state, retrieval, safety, permissions, and output. It moves from classifying surfaces, versions, and provenance to tracing action loops, comparing work models, and reading conditional safeguards. The archive reveals declared architecture and design priorities, but cannot alone prove authenticity, completeness, current deployment, or runtime performance.
Learning outcomes
- A system prompt exposes a product contract, not the model’s whole behavior.
- Surface, version, provenance, and capture boundary determine what a prompt can support.
- Tools become reliable only when routing, authorization, observation, and failure reporting align.
- Prompt captures reveal declared architecture, but runtime claims need corroboration and testing.
Keywords
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PlansFAQ
What is the asgeirtj/system_prompts_leaks archive useful for?
The asgeirtj/system_prompts_leaks archive is useful for analyzing declared AI product architecture across captured roles, tools, state, safety, and output rules.
How should a reader compare prompts in asgeirtj/system_prompts_leaks?
A reader should compare product surfaces, modes, dates, provenance, role, authorized action, state, retrieval, output, and verification—not model names or prompt length.
What can a captured prompt prove about an AI product?
A captured prompt can show what the repository records and what the file specifies; it cannot alone prove complete, current, or reliably enforced runtime behavior.
Why do system prompts differ between chat, search, and coding products?
System prompts differ because each product surface has different work models, tools, state, authority, output contracts, and consequences of error.