Principal PM Dennis Yang uses the AI-powered IDE Cursor not for coding, but as a central workspace for writing PRDs in Markdown, managing them with Git, and connecting to tools like Jira and Confluence. This consolidates the PM workflow into a developer-centric environment.

The power of tools like Claude Code comes from giving the AI access to fundamental command-line tools (e.g., `bash`, `grep`). This allows the AI to compose novel solutions and lets product teams define new features using simple English prompts rather than hard-coded logic.

AI code editors can be tasked with high-level goals like "fix lint errors." The agent will then independently run necessary commands, interpret the output, apply code changes, and re-run the commands to verify the fix, all without direct human intervention or step-by-step instructions.

Because AI agents operate autonomously, developers can now code collaboratively while on calls. They can brainstorm, kick off a feature build, and have it ready for production by the end of the meeting, transforming coding from a solo, heads-down activity to a social one.

Use Claude's "Artifacts" feature to generate interactive, LLM-powered application prototypes directly from a prompt. This allows product managers to test the feel and flow of a conversational AI, including latency and response length, without needing API keys or engineering support, bridging the gap between a static mock and a coded MVP.

While "vibe coding" tools are excellent for sparking interest and building initial prototypes, transitioning a project into a maintainable product requires learning the underlying code. AI code editors like Cursor act as the next step, helping users bridge the gap from prompt-based generation to hands-on software engineering.

Using plain-English rule files in tools like Cursor, data teams can create reusable AI agents that automate the entire A/B test write-up process. The agent can fetch data from an experimentation platform, pull context from Notion, analyze results, and generate a standardized report automatically.

The best agentic UX isn't a generic chat overlay. Instead, identify where users struggle with complex inputs like formulas or code. Replace these friction points with a native, natural language interface that directly integrates the AI into the core product workflow, making it feel seamless and powerful.

AI development has evolved to where models can be directed using human-like language. Instead of complex prompt engineering or fine-tuning, developers can provide instructions, documentation, and context in plain English to guide the AI's behavior, democratizing access to sophisticated outcomes.