Combine specialized AI tools in sequence. Use one tool (like "Last 30 Days") to research a trending market signal, then feed that context into another (like "Compound Engineering") to generate a business plan and technical architecture, drastically accelerating the ideation-to-development pipeline.

You don't need technical skills to build custom AI tools. Frame your needs as problem statements to a capable AI agent. The AI then acts as a product manager, asking clarifying questions to understand the requirements before generating the necessary scripts and workflows to solve your problem automatically.

Before writing any code, instruct the AI to act as an expert company builder like Sam Altman. It will then interview you, pushing back on vague answers to uncover flaws in your logic, ultimately producing a battle-tested product spec with a higher chance of success.

Standard LLMs often validate ideas to be helpful. Implement a structured "viability gate" skill with clear evaluation criteria (e.g., problem clarity, competition) designed to explicitly recommend abandoning unpromising projects, saving valuable time and resources.

To get the best results from AI code generation platforms, first use a conversational LLM like Claude to brainstorm and write a detailed product spec. This two-step process—spec generation then code generation—improves the final output and reduces costly iterations with the coding agent.

The traditional product workflow—writing PRDs, waiting for mocks, then building a prototype—is being collapsed by agentic tools. A single "Builder PM" can now perform user research, generate PRDs, create functional mocks, and build a working prototype, drastically shortening the feedback loop.

Treat AI 'skills' as Standard Operating Procedures (SOPs) for your agent. By packaging a multi-step process, like creating a custom proposal, into a '.skill' file, you can simply invoke its name in the future. This lets the agent execute the entire workflow without needing repeated instructions.

Dramatically accelerate product development by "tool-hopping": use Perplexity for research, feed results to a custom ChatGPT for a PRD, generate a UI prototype with V0 from the PRD, and create a promotional video with Flow or Sora for stakeholder buy-in.

Product Managers at Ramp now write specs with the primary audience being an AI agent. The spec is effectively a prompt, and its output is a working product, not just a document for engineers to interpret. This changes the entire dynamic of product definition from documentation to direct creation.