AI tools have the "half-life of a flea." Instead of chasing the latest platform, product managers should focus on mastering fundamental techniques—like context engineering or problem-solving—which are transferable and will outlast any single tool.

Structure AI context into three layers: a short global file for universal preferences, project-specific files for domain rules, and an indexed library of modular context files (e.g., business details) that the AI only loads when relevant, preventing context window bloat.

AI models are stateless and "forget" between tasks. The most effective strategy is to create a comprehensive "context library" about your business. This allows you to onboard the AI in seconds for any new task, giving it the equivalent of years of company-specific training instantly.

Most users re-explain their role and situation in every new AI conversation. A more advanced approach is to build a dedicated professional context document and a system for capturing prompts and notes. This turns AI from a stateless tool into a stateful partner that understands your specific needs.

Frame your personal and professional information as a structured set of machine-readable files. This "operating manual" allows AI agents to understand your roles, goals, and constraints without constant re-explanation, just as a developer uses API docs to interact with software.

The underlying system of text files defining your identity, context, and skills is portable across different AI tools. As agentic tools converge in capability, this foundational 'OS' becomes your most valuable, enduring asset, making tool selection a less critical decision.

AI has no memory between tasks. Effective users create a comprehensive "context library" about their business. Before each task, they "onboard" the AI by feeding it this library, giving it years of business knowledge in seconds to produce superior, context-aware results instead of generic outputs.

Instead of relying on platform-specific, cloud-based memory, the most robust approach is to structure an agent's knowledge in local markdown files. This creates a portable and compounding 'AI Operating System' that ensures your custom context and skills are never locked into a single vendor.

Focusing on refining prompts (skills) yields diminishing returns. The breakthrough in AI content quality comes from building a 'foundational layer' of shared intelligence—core documents defining your audience, voice, and positioning—that every AI skill draws from, preventing it from starting from zero each time.