Effective prompt engineering for AI agents isn't an unstructured art. A robust prompt clearly defines the agent's persona ('Role'), gives specific, bracketed commands for external inputs ('Instructions'), and sets boundaries on behavior ('Guardrails'). This structure signals advanced AI literacy to interviewers and collaborators.

Instead of manually crafting a system prompt, feed an LLM multiple "golden conversation" examples. Then, ask the LLM to analyze these examples and generate a system prompt that would produce similar conversational flows. This reverses the typical prompt engineering process, letting the ideal output define the instructions.

While Claude's built-in 'create skill' tool is clunky, its output reveals a highly structured template for effective prompts. It includes decision trees, clarifying questions for the user, and keywords for invocation, serving as an invaluable guide for building robust skills without starting from scratch.

Browser-based ChatGPT cannot execute code or connect to external APIs, limiting its power. The Codex CLI unlocks these agentic capabilities, allowing it to interact with local files, run scripts, and connect to databases, making it a far more powerful tool for real-world tasks.

Instead of prompting a specialized AI tool directly, experts employ a meta-workflow. They first use a general LLM like ChatGPT or Claude to generate a detailed, context-rich 'master prompt' based on a PRD or user story, which they then paste into the specialized tool for superior results.

Before delegating a complex task, use a simple prompt to have a context-aware system generate a more detailed and effective prompt. This "prompt-for-a-prompt" workflow adds necessary detail and structure, significantly improving the agent's success rate and saving rework.

Instead of managing prompts in a separate library, save them as custom commands directly within your Claude Code project folder. This lets you trigger complex, multi-file prompts with a simple command (e.g., `/meeting_notes`), embedding powerful, recurring workflows directly into your development environment.

To get consistent, high-quality results from AI coding assistants, define reusable instructions in dedicated files (e.g., `prd.md`) within your repository. This "agent briefing" file can be referenced in prompts, ensuring all generated assets adhere to a predefined structure and style.

The most effective way to build a powerful automation prompt is to interview a human expert, document their step-by-step process and decision criteria, and translate that knowledge directly into the AI's instructions. Don't invent; document and translate.