Consolidate your values, goals, and principles into a single document. Upload this "master prompt" to an AI before any query, ensuring all responses are tailored to your unique context. This transforms a generic tool into a personalized advisor that understands you deeply.
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People struggle with AI prompts because the model lacks background on their goals and progress. The solution is 'Context Engineering': creating an environment where the AI continuously accumulates user-specific information, materials, and intent, reducing the need for constant prompt tweaking.
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.
Instead of using AI to generate generic text, leverage it as a partner to enhance your unique voice. A powerful technique is to have AI interview you to create a "story log"—a database of your personal anecdotes and experiences. This provides authentic, non-replicable material for future content.
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.
Feed your personal writings—journals, blog posts, or content—into an AI. Then, ask it to identify unique traits or patterns about you that you might not see in yourself. This leverages AI's pattern recognition for deep self-reflection and uncovering unconscious biases or strengths.
Moving beyond simple commands (prompt engineering) to designing the full instructional input is crucial. This "context engineering" combines system prompts, user history (memory), and external data (RAG) to create deeply personalized and stateful AI experiences.
Go beyond using AI for research by codifying your North Star, OKRs, and strategic goals into a personalized AI agent. Before important meetings, use this agent as a 'thought partner' to pressure-test your ideas, check for alignment with your goals, and identify blind spots. This 10-minute exercise dramatically improves meeting focus and outcomes.
The skills of setting clear goals, understanding resource (model) strengths, and defining processes are the same for managing people and AI agents. Being a great manager makes you a great AI user, as both require clarifying outcomes and marshalling resources to achieve them.
To avoid robotic content, use “humanization prompting.” This involves uploading transcripts of your natural speech (from interviews or voice notes) to a custom GPT’s knowledge base, training it to adopt your unique cadence, vocabulary, and style.
