llm-tuning-patterns

What it does

This skill provides a library of prompt engineering techniques designed to optimize Large Language Model outputs for specific tasks. It enables agents to dynamically adjust their reasoning, formatting, and constraint adherence based on the provided pattern context.

When to use it

• Refining complex logic chains where standard prompting yields inconsistent results.
• Enforcing strict output formats or schemas in data processing workflows.
• Improving role-playing scenarios by injecting specific behavioral constraints.
• Debugging model hallucinations by applying targeted verification patterns.

Key capabilities

• Dynamic pattern injection for task-specific optimization.
• Enhanced reasoning structures for multi-step problems.
• Constraint enforcement mechanisms for output formatting.
• Context-aware adaptation of tone and style.

Example prompts

• "Apply the chain-of-thought pattern to solve this math problem step-by-step, ensuring no steps are skipped."
• "Use the json-schema-validation pattern to extract entities from this text and return only valid JSON."
• "Activate the role-play-architect pattern to generate a project plan as if you were a senior CTO."

Tips & gotchas

Ensure the target model supports the specific syntax or tokens required by the chosen pattern, as not all LLMs interpret structural markers identically. Start with simple patterns before combining multiple advanced techniques to avoid prompt injection risks or token limit overflows.