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.
Despite general AI hype, the demand for AI Product Managers (AIPMs) is real, reflected in median compensation packages that are now competitive with top-tier software engineering roles in major tech hubs like the Bay Area.
To find valuable AI use cases, start with projects that save time (efficiency gains). Next, focus on improving the quality of existing outputs. Finally, pursue entirely new capabilities that were previously impossible, creating a roadmap from immediate to transformative value.
The key technical skill for an AI PM is not deep knowledge of model architecture but a higher-level understanding of how to orchestrate AI components. Knowing what AI can do and how systems connect is more valuable than knowing the specifics of fine-tuning or RAG implementation.
Prototyping and even shipping complex AI applications is now possible without writing code. By combining a no-code front-end (Lovable), a workflow automation back-end (N8N), and LLM APIs, non-technical builders can create functional AI products quickly.
Beyond being a revenue stream, teaching can be a strategic tool for AI professionals. A foundational course provides user insights and product ideas, while an advanced course creates a community of experts who help solve real-world technical challenges for the instructor's primary business.