To improve an agent's performance on a specific task like prompting the VO3 video model, create a dedicated 'onboarding document'. Use a tool like Perplexity to gather best practices from experts, compile them into a doc, and instruct the agent to reference it. This shortcuts the learning curve and embeds expertise.
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Frame your interaction with AI as if you're onboarding a new employee. Providing deep context, clear expectations, and even a mental "salary" forces you to take the task seriously, leading to vastly superior outputs compared to casual prompting.
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.
Frame AI agent development like training an intern. Initially, they need clear instructions, access to tools, and your specific systems. They won't be perfect at first, but with iterative feedback and training ('progress over perfection'), they can evolve to handle complex tasks autonomously.
Instead of codebases becoming harder to manage over time, use an AI agent to create a "compounding engineering" system. Codify learnings from each feature build—successful plans, bug fixes, tests—back into the agent's prompts and tools, making future development faster and easier.
To master a new skill like creating a sales offer, first command an LLM to outline the framework of a known expert (e.g., Alex Hormozi). Then, have it generate interview questions based on that framework. Answering these allows the LLM to apply the expert's model directly to your specific situation.
Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.
A truly effective skill isn't created in one shot. The best practice is to treat the first version as a draft, then iteratively refine it through research, self-critique, and testing to make the AI "think like an expert, not just follow steps."
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.
Most users get poor results from creative AI due to complex prompting. AI agent tools act as an intermediary layer, handling the expert-level prompting and workflow automation. This makes advanced, professional-quality results accessible to beginners without a steep learning curve.
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.
