At OpenAI, PMs who aren't strong coders use Codex to build features 70-80% of the way to completion, especially when engineering has no bandwidth. This transforms the PM role from a spec-writer to a builder, providing functional prototypes instead of just documents.

Field engineers can bypass documentation limitations by querying the entire codebase with AI tools like Claude Code. This provides detailed, step-by-step answers that public docs lack, directly addressing complex customer problems and reducing reliance on the engineering team.

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.

To improve communication with engineering, PMs should use AI to analyze their company's actual codebase. Asking the AI for a high-level architecture diagram or to explain a component is a practical way to learn the system and develop a shared language with developers.

Moving PRDs and other product artifacts from Confluence or Notion directly into the codebase's repository gives AI coding assistants persistent, local context. This adjacency means the AI doesn't need external tool access (like an MCP) to understand the 'why' behind the code, leading to better suggestions and iterations.

OpenAI structures its repositories to be a complete, self-contained knowledge base for AI agents. All project artifacts—design docs, historical implementation plans, and even text versions of external library documentation—are checked in, allowing the agent to find any needed context via simple search.

Instead of codebases becoming harder to manage over time, use an AI agent to create a "compounding engineering" system. Codify learnings from each feature build—successful plans, bug fixes, tests—back into the agent's prompts and tools, making future development faster and easier.

Instead of manually learning and implementing complex design techniques you find online, feed the URL of the article or example directly to an AI coding assistant. The AI can analyze the technique and apply it to your existing components, saving significant time.

For complex projects with many files, prompt Claude to create a "workspace map" of the folder. This map acts as an index, helping the AI quickly find relevant information without ingesting every file, which saves tokens and improves response speed and accuracy.