Instead of using single prompts like "write a blog post," build a custom AI "skill." Codify your entire creative process by breaking the task into sub-steps (e.g., research, synthesis, outlining), with human-in-the-loop approvals to guide the output and inject your unique taste.

Writer Katie Parrott achieved massive output by first building foundational documents: style guides, audience personas, and product specifics. This "context engineering" creates guardrails for the AI, ensuring consistent, on-brand results and turning a manual process into a repeatable, high-speed system.

Instead of a single complex prompt, break down marketing tasks into a series of smaller, single-purpose AI skills. For example, a content workflow can be chained: one skill for drafting, one for HTML generation, and another for platform-specific formatting. This modularity improves reliability and scalability.

AI agents can overcomplicate instructions and create 'AI sprop' (slop/propaganda). To combat this, build a dedicated 'skill editor' skill that runs on other skills to make them more concise, remove repetitive instructions, and maintain clarity in your automations.

To move beyond basic AI tasks, chain multiple skills together. A "skill chain" runs a sequence of specialized AI skills—like drafting, copywriting, and quality assurance—to produce a complex output with higher fidelity and less human intervention.

Don't rely on a single, general AI prompt. Create a portfolio of specialized AI agents, each trained and instructed for a distinct function like prospecting, blog writing, or industry analysis, effectively mimicking a real marketing team's structure.

Instead of building one monolithic skill for a large process, break it into a sequence of smaller, independent skills. This "skill chain" approach is ideal if any sub-task (e.g., generating just a thumbnail or a title) might need to be run on its own, promoting modularity and reusability.

Instead of prompting an AI to generate a full article, which often results in 'slop,' a better approach is to use it as an assembly tool. Feed the AI granular, pre-vetted pieces of unique business intelligence (like sales data or expert insights) to construct a higher-quality output.

Instead of asking an LLM to generate a full email, create a workflow where it produces individual sections, each with its own specific strategy and prompt. A human editor then reviews the assembled piece for tone and adds "spontaneity elements" like GIFs or timely references to retain a human feel.