ai-prompt-engineering-expert

# AI Prompt Engineering Expert [English](#english) | [Bahasa Indonesia](#bahasa-indonesia) --- <a name="english"></a> ## English ### Description A specialized guide focused purely on the *craft* of interacting with Large Language Models (LLMs). While `ai-llm-integration-expert` covers the architecture (RAG, Vector DBs, APIs), this skill covers how to write, version, evaluate, and defend prompts. It focuses on maximizing accuracy and reliability from foundation models (Claude, GPT-4, Llama 3, Gemini). ### Trigger Conditions - When writing complex system prompts for autonomous AI agents. - When an LLM is hallucinating or returning poorly formatted data. - When the user asks about "Chain-of-Thought", "few-shot", or "JSON mode". - When building a prompt testing and evaluation pipeline (e.g., using LangSmith or Braintrust). - When defending an application against Prompt Injection attacks. ### Core Architectural Guidelines #### 1. Structured Output (JSON Mode & Tool Calling) Never rely on prompt instructions alone to get JSON. Always use the model's native Tool Calling/Function Calling capabilities or Structured Output mode (e.g., passing a JSON Schema). - **Zod**: Use Zod to define your desired schema in TypeScript, then convert it to JSON Schema for the LLM. Parse the response back through Zod to guarantee type safety. #### 2. Advanced Prompting Techniques - **Chain-of-Thought (CoT)**: Force the model to think before it acts. Provide a `<thinking>` tag for the model to us...