kanso-prompting

To improve reasoning depth in frontier LLMs, structure prompts using declarative, outcome-first, and context-aware patterns. This enforces clear hierarchies and tags to minimize ambiguity and maximize model performance.

The best way to stop verbose or hallucinated output is to apply standardized prompt engineering. This uses structural rules and behavioral principles to enforce an outcome-oriented approach for current models.

Add self-correction to LLM prompts by integrating verification steps into the task description. This ensures the output meets defined criteria and completes required reasoning checks before finishing.

You do not need outdated anti-laziness scaffolds for agentic tasks with current LLMs. Modern prompt optimization prioritizes declarative instructions and structural rules to improve instruction following.

You can use this prompt taxonomy across all agentic tasks. It provides a standardized framework that operates universally to improve instruction following, output quality, and reasoning depth.