Expert-level prompting isn't about writing one-off commands. The advanced technique is to find effective prompt frameworks (e.g., a leaked system prompt), distill the core principles, and train a custom GPT on that methodology. This creates a specialized AI that can generate sophisticated prompts for you.

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.

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.

Including the special keyword "ultra think" in a prompt signals Claude Code to engage in a more intensive thought process. This undocumented feature can significantly improve the quality of output for difficult bugs or complex tasks without noticeably impacting token usage.

Instead of manually crafting complex instructions, first iterate with an AI until you achieve the perfect output. Then, provide that output back to the AI and ask it to write the 'system prompt' that would have generated it. This reverse-engineering process creates reusable, high-quality instructions for consistent results.

To correct an AI's output when it's off track, use numerical multipliers to signal a dramatic shift. Instead of vague feedback, prompts like "be 100x more direct" or "make this 10x more creative" give the model a quantitative instruction to escalate its response, leading to more significant adjustments.

Instead of struggling to craft an effective prompt, users can ask the AI to generate it for them. Describe your goal and ask ChatGPT to 'write me the perfect ChatGPT prompt for this with exact wording, format, and style.' This meta-prompting technique leverages the AI's own capabilities for better results.

Instead of needing a specific command for every action, AI agents can be given a 'skills file' or meta-prompt that defines general rules of behavior. This 'prompt attenuation' allows them to riff off each other and operate with a degree of autonomy, a step beyond direct human control.

When a prompt yields poor results, use a meta-prompting technique. Feed the failing prompt back to the AI, describe the incorrect output, specify the desired outcome, and explicitly grant it permission to rewrite, add, or delete. The AI will then debug and improve its own instructions.