Optimize prompts for Large Language Models using expert techniques.
npx skills add https://github.com/slantview/claude --skill prompt-engineer-slantview
Please help me install this Agent Skill. Skill: prompt-engineer Source: https://github.com/slantview/claude/tree/main/skills/prompt-engineer Command: npx skills add https://github.com/slantview/claude --skill prompt-engineer-slantview
This Skill helps users create and optimize prompts for Large Language Models (LLMs) and AI systems, ensuring better performance and more accurate outputs.
Use the prompt-engineer skill to create a prompt for summarizing technical documents.
High-intent search queries and answers about installing and using this skill.
To optimize prompts for large language models, you refine existing instructions for clarity and apply advanced techniques like Chain-of-Thought and Constitutional AI to improve output accuracy.
The best way to design system prompts for AI features is to establish a clear prompt structure tailored to model-specific nuances, ensuring the AI system receives robust instructions for effective guidance.
Chain-of-Thought prompting works for LLM optimization by structuring instructions to guide the model through sequential reasoning steps, enhancing the accuracy and logic of complex AI outputs.
You can adapt existing prompts for different AI systems by adjusting the prompt structure to align with model-specific nuances, ensuring the instructions perform effectively across various platforms.
You need Constitutional AI in prompt design when your AI feature requires strict adherence to safety and behavioral guidelines, ensuring the model's outputs remain controlled and aligned with defined principles.
An LLM prompt produces inaccurate outputs when it lacks clear structure and model-specific adaptation, requiring optimization through documented processes and advanced techniques to refine the instructions.
shirulotGenerate product specification documents with Google AI Studio capabilities.
JHostalekCreate and refine LLM prompts with explicit output contracts and constraints.
Blake-JohnWrite, refactor, and evaluate LLM prompts with JSON schemas.
mikersaysAudits a session and recommends user context to preserve for the next agent.
