prompting

To write reliable system prompts for coding agents, define the assistant's role, constraints, and output format using structured, model-agnostic best practices. This ensures consistent guidance across tasks and different LLMs.

The best way to structure task prompts for LLMs is to use a three-part description with a vector-friendly request and metadata fields. This format improves reliability and constraint definition across various coding assistants.

Tree of Thoughts (ToT) improves prompt engineering workflows by applying prompt optimization techniques to enhance prompt quality at scale. It works alongside ReAct and Atom of Thoughts (AoT) to generate reliable, high-quality prompts.

Yes, you can use meta-prompting techniques like OPRO and GEPA for coding assistants. These prompt optimization workflows improve prompt quality at scale and ensure model-agnostic performance across different coding tasks.

No specific dependencies are required to implement model-agnostic prompting. You only need a SKILL.md file with YAML frontmatter containing a name, description, complexity, category, and keywords to start crafting reliable prompts.