Atlassian found users struggled with prompting, using vague language like 'change logo to JIRA' which caused the AI to pull old assets. They embedded pre-written, copyable commands into their prototyping templates. This guides users to interact with the underlying code correctly, reducing hallucinations and boosting confidence.

Instead of typing, dictating prompts for AI coding tools allows for faster and more detailed instructions. Speaking your thought process naturally includes more context and nuance, which leads to better results from the AI. Tools like Whisperflow are optimized with developer terminology for higher accuracy.

Capable AI coding assistants allow PMs to build and test functional prototypes or "skills" in a single day. This changes the product development philosophy, prioritizing quick validation with users over creating detailed UI mockups and specifications upfront.

Creating user manuals is a time-consuming, low-value task. A more efficient alternative is to build an AI chatbot that users can interact with. This bot can be trained on source engineering documents, code, and design specs to provide direct answers without an intermediate manual.

Instead of a multi-week process involving PMs and engineers, a feature request in Slack can be assigned directly to an AI agent. The AI can understand the context from the thread, implement the change, and open a pull request, turning a simple request into a production feature with minimal human effort.

At OpenAI, the development cycle is accelerated by a practice called "vibe coding." Designers and PMs build functional prototypes directly with AI tools like Codex. This visual, interactive method is often faster and more effective for communicating ideas than writing traditional product specifications.

A practical AI workflow for product teams is to screenshot their current application and prompt an AI to clone it with modifications. This allows for rapid visualization of new features and UI changes, creating an efficient feedback loop for product development.

The most leveraged engineering activity is creating a 'meta-prompt' that takes a simple feature request and automatically generates a detailed technical specification. This spec then serves as a high-quality prompt for an AI coding agent, making all future development faster.

Create a powerful research workflow by extracting text from relevant Reddit threads and feeding it into ChatGPT. Prompt the AI to summarize the most common topics, questions, and pain points. This quickly distills the core language and concerns of a niche community, informing content and product strategy.