A 'skill' is a static AI command. A 'loop' elevates this by building a flywheel where the system learns from its own output and interactions, automatically feeding those learnings back to improve future performance.
Standard prompting is a conversational back-and-forth. An 'agentic' system is given a goal and a method to verify its own work, allowing it to operate autonomously and take the human out of the loop to achieve scale.
Treat your AI skills and loops like code by managing them in Git. This provides a crucial safety net, allowing you to instantly roll back to a previously effective version if a new LLM update causes performance to decline.
When a new AI model degrades a skill, avoid a full rollback. Instead, 'revert forward' by modifying the current version to reintroduce the specific lost behaviors. This preserves any new benefits from the model update while fixing the regression.
The primary AI bottleneck isn't idea generation, but validation. Feed customer research data (call transcripts, survey data) into an AI to create 'synthetic customers' that can give initial feedback on prototypes, quickly filtering out bad ideas before engaging real users.
As AI dramatically accelerates building, the bottleneck shifts from development to customer validation. This necessitates a cultural shift where the entire product team, not just PMs or researchers, interacts with customers weekly to keep pace.
Stop writing one-size-fits-all docs. Create two types: lengthy, exhaustive documents designed purely for AI ingestion to provide deep context, and highly succinct, visual (1-3 page) documents designed for quick human consumption and decision-making.
Doubling shipping speed with AI also doubles the number of bugs customers encounter, even if the defect *rate* is unchanged. Engineering loops must leverage AI to improve quality and testing, not just accelerate development, to avoid degrading the user experience.
Allowing PMs to commit code is not a cultural choice but a reflection of system maturity. It's safe only with robust CI/CD and automated testing. In immature systems, it creates shadow work for engineers and introduces risk.
AI is blurring the lines between traditional product roles. The emerging 'Product Builder' is a single individual who can leverage AI to handle the entire lifecycle from customer problem to shipped solution, dramatically increasing speed and leverage.
Do not rely on natural language prompts to prevent an AI from taking dangerous actions (e.g., deleting files). Instead, build deterministic 'hooks' into the system that trigger on specific commands, providing a reliable safety layer that the AI cannot ignore.