The transcript does not explicitly list the core capabilities of Claude 4. While the video mentions that the prompt establishes the model's core capabilities early on, the specific capabilities are not detailed in the provided transcript. To answer your question, a direct quote or timestamp referencing the specific capabilities would be needed.
This video analyzes a leaked system prompt for Claude 4, exploring seven prompting strategies revealed within it. The speaker acknowledges ethical ambiguity regarding leaked prompts but focuses on the valuable insights they offer for improving prompt engineering. The video shifts the perspective from simply instructing the model to a focus on building policies to prevent failure modes, arguing that this approach leads to higher-quality outputs.
Instantiating Identity Upfront: Establishing stable context early reduces the model's working memory burden, leading to more consistent responses.
Triggers and Template Refusals: Explicit "if-then-y" conditional blocks handle edge cases, preventing inconsistencies caused by ambiguity. Clear boundaries and conditionals are emphasized.
Three-Tier Uncertainty Routing: A decision tree guides the model's handling of ambiguity, differentiating between timeless information, slowly changing information, and live information requiring immediate search. This shows how decision criteria, not just commands, improve model performance.
Lock Tool Grammar: Providing both correct and incorrect examples when instructing the model to use tools (APIs, etc.) improves its understanding of tool usage. This emphasizes the power of negative examples in model training.
Binary Style Rules: Using clear on/off rules instead of subjective guidelines enhances model clarity and response consistency. The prompt prioritizes absolute rules over interpretable adjectives.
Positional Reinforcement: Repeating critical instructions at strategic positions throughout the prompt combats attention degradation in long contexts, reinforcing key rules at intervals. This is analogous to providing speed limit reminders.
Post-Tool Reflection: Including a "thinking pause" after tool use allows the model to better process outputs and improve accuracy, especially in complex, multi-step reasoning tasks. This encourages a cognitive checkpoint for improved decision-making.
