How this PM uses AI for PRDs, JIRA tickets, and replying to coworkers | Dennis Yang (Chime)
Audio Brief
Show transcript
This episode explores leveraging developer-focused AI tools for non-coding tasks, emphasizing interoperability and agentic workflows to automate documentation.
There are four key takeaways from this discussion. First, adopt developer centric tools and principles like interoperability and version control for non-coding work to build more integrated systems. Second, automate the full documentation lifecycle using two-way AI agents that push content to platforms and pull context back. Third, prototype new AI agents and products using natural language within a "Super MVP" markdown file to rapidly test workflows. Fourth, engage with AI tools consistently every day to build an intuitive understanding of their capabilities and limitations.
The conversation highlights integrating developer concepts like Markdown and Git into general productivity. This approach applies powerful, version-controlled content creation methods to product management and writing. It ensures data portability, avoids lock-in, and enables seamless connections between tools like Confluence, Notion, and Jira.
Multi-step AI agents manage complex, round-trip tasks. These agents publish documents to systems like Confluence and retrieve critical context, such as published URLs or user comments, back to the original document. This agentic, two-way workflow significantly reduces administrative toil, freeing professionals for strategic work.
A novel "Super MVP" concept enables zero-code rapid prototyping of new AI agents. Product managers build and test agents by writing detailed natural language instructions directly within a markdown file. An AI tool then executes these instructions, allowing quick iteration and refinement of complex AI workflows before committing engineering resources.
Consistent daily interaction with AI tools is crucial for mastery. Regular engagement helps users develop an intuitive understanding of their evolving strengths, weaknesses, and optimal applications. This continuous interaction is vital for recognizing improvements or regressions and effectively leveraging AI for maximum benefit.
By embracing these principles, professionals can significantly enhance productivity and strategic focus in an AI-powered future.
Episode Overview
- This episode explores how to use developer-focused AI tools, like the code editor Cursor, for non-coding tasks such as product management and writing.
- It emphasizes the critical importance of interoperability, showing how to build a workflow that seamlessly connects tools like Confluence, Notion, and Jira without data lock-in.
- The core of the conversation is a demonstration of an end-to-end "agentic" workflow that automates the entire lifecycle of a Product Requirements Document (PRD), from creation to feedback management.
- A novel concept called the "Super MVP" is introduced, which involves prototyping new AI agents using natural language instructions in a markdown file before writing any code.
Key Concepts
- Agentic, Two-Way Workflows: The podcast showcases multi-step AI agents that can manage complex, round-trip tasks. This includes not only publishing documents to systems like Confluence but also bringing back context, such as the published URL or user comments, to the original document.
- Interoperability as a Core Principle: A central theme is the preference for tools that allow data to be portable and accessible across various applications, actively avoiding "walled garden" systems that lock in content.
- Prototyping AI with AI (The "Super MVP"): An advanced technique is demonstrated where a product manager can build and test a new AI agent by writing detailed natural language instructions in a markdown file, which Cursor then executes. This serves as a "zero-code" method for rapid prototyping.
- Convergence of Developer and Productivity Tools: The conversation highlights a trend where developer concepts like Markdown, Git version control, and command-line interactions are being integrated into general productivity workflows, enhancing efficiency and collaboration.
- Reducing Toil & Cognitive Load: By automating repetitive and administrative tasks (e.g., formatting, status reporting, ticket creation), AI frees up product managers to focus their mental energy on higher-value strategic work.
Quotes
- At 0:38 - "The most useful solutions will have interoperability as one of the key things." - Dennis states his core belief about what makes a modern software solution valuable.
- At 20:53 - "It's not just one way with these MCPs... you can actually do that round trip of context back into what you're working on." - Rachel is impressed by the AI's ability to not only publish a document but also update the original file with the new Confluence link.
- At 32:37 - "I'm reducing the time I'm spending writing status and at the same time improving the status content that is being circulated." - Dennis summarizes the dual benefit of using AI for routine tasks like generating status reports.
- At 39:12 - "If you're not using it every single day, you will not notice when things get better or worse." - Rachel emphasizes that consistent, daily interaction with AI tools is the best way to build an intuition for their capabilities and limitations.
- At 40:51 - "I use Cursor itself to continue to prototype what I call typically a 'Super MVP'... I'm using AI, to prototype the AI product that I'm about to build." - Dennis describes his advanced workflow for creating and testing new AI agents using natural language instructions instead of code.
Takeaways
- Adopt developer-centric tools and principles like interoperability and version control for non-coding work to build more powerful and integrated systems.
- Automate the full lifecycle of documentation by creating "two-way" AI agents that not only push content to other platforms but also pull context back, reducing administrative work.
- Prototype new AI agents and products using natural language in a "Super MVP" markdown file to rapidly test and refine complex workflows before committing engineering resources.
- To truly master AI tools, engage with them consistently every day to build an intuitive understanding of their strengths, weaknesses, and evolving capabilities.