Ask an AI to write the product spec for a feature. If it feels wrong, re-prompt instead of editing. Then, have the AI generate a prompt for an image generator to create a visual mockup, allowing you to see the feature before committing to code.

Before writing any code for a complex feature or bug fix, delegate the initial discovery phase to an AI. Task it with researching the current state of the codebase to understand existing logic and potential challenges. This front-loads research and leads to a more informed, efficient approach.

The goal isn't to build one perfect prototype quickly. The real strategic advantage of AI tools is the ability to generate three or four distinct variations of a feature in a short time. This allows teams to explore a wider solution space and make better decisions after hands-on testing.

When stuck on product direction, use a simple prompt like "add five new features." The AI acts as a creative partner, generating ideas you may not have considered. Even if most are discarded, this technique can spark inspiration and uncover valuable additions.

Beyond automating repetitive tasks, AI's power lies in being a thought partner. Use it for an iterative, "ping pong style" back-and-forth to develop ideas, conduct deep market research, and rapidly get up to speed on new domains. This compresses the learning curve and leads to more nuanced strategies.

Railway encourages its team to use AI not just for coding but to build massive test benches and prototypes of future product concepts. This allows them to validate complex ideas for free, accelerate learning, and in some cases, skip incremental roadmap items to build the final vision sooner.

PMs can use AI agents connected to their codebase to explore technical feasibility and iterate on ideas. This serves as a 'digital tech lead,' saving immense time for senior engineers who were previously burdened with speculative 'how hard would it be?' questions from product managers.

Use a dedicated AI chat as a dynamic feature backlog. Continuously feed it new ideas and user feedback, prompting the AI to maintain a ranked table of features based on estimated build time and potential impact. This creates a low-friction system for choosing what to build next during focused work sprints.

Historically, resource-intensive prototyping (requiring designers and tools like Figma) was reserved for major features. AI tools reduce prototype creation time to minutes, allowing PMs to de-risk even minor features with user testing and solution discovery, improving the entire product's success rate.