Instead of one large context file, create a library of small, specific files (e.g., for different products or writing styles). An index file then guides the LLM to load only the relevant documents for a given task, improving accuracy, reducing noise, and allowing for 'lazy' prompting.

The quality of an AI-generated application is directly tied to the context provided. By uploading a detailed document, such as a book chapter on creator marketing, the AI can build a highly specific and nuanced application that reflects the user's unique frameworks and knowledge.

With AI agents, the key to great results is not about crafting complex prompts. Instead, it's about 'context engineering'—loading your agent with rich information via files like 'agents.md'. This allows simple commands like 'write a cold email' to yield highly customized and effective outputs.

"Skills" are markdown files that provide an AI agent with an expert-level instruction manual for a specific task. By encoding best practices, do's/don'ts, and references into a skill, you create a persistent, reusable asset that elevates the AI's performance almost instantly.

For maximum effectiveness, keep agent instructions lean ('thin agents') and pack detailed, step-by-step context into the skills themselves ('thick skills'). This ensures the core logic resides in the skill, allowing any agent or AI harness to execute the task consistently with minimal setup.

A generalist AI agent's audit produces generic copy. However, feeding that audit's findings to a second, specialized copywriting agent results in significantly better headlines. This demonstrates the power of creating multi-agent, purpose-built workflows for complex tasks.

Move beyond the prompt by creating local folders containing brand guidelines, founder writing samples, ICP lists, and case studies. When your AI agent can access these files, its output transforms from generic to highly usable and on-brand, dramatically improving quality.

A robust AI 'skill' is more than a prompt; it's a folder. It contains the core instructions plus reference files like templates, playbooks, and scoring models. This allows the AI to ground its execution in your company's specific context.

Effective AI marketing requires first building a structured system of folders and context files (brand voice, ICPs). This foundational work enables consistent, high-quality outputs and is more effective than ad-hoc prompting. It's about working slow first to eventually work fast.