To prevent an AI agent from repeating mistakes across coding sessions, create 'agents.md' files in your codebase. These act as a persistent memory, providing context and instructions specific to a folder or the entire repo. The agent reads these files before working, allowing it to learn from past iterations and improve over time.

Structure AI context into three layers: a short global file for universal preferences, project-specific files for domain rules, and an indexed library of modular context files (e.g., business details) that the AI only loads when relevant, preventing context window bloat.

To create detailed context files about your business or personal preferences, instruct your AI to act as an interviewer. By answering its questions, you provide the raw material for the AI to then synthesize and structure into a permanent, reusable context file without writing it yourself.

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.

The easiest way to teach Claude Code is to instruct it: "Don't make this mistake again; add this to `claude.md`." Since this file is always included in the prompt context, it acts as a permanent, evolving set of instructions and guardrails for the AI.

The 'Claudie' AI project manager reads a core markdown file every time it runs, which acts as a permanent job description. This file defines its role, key principles, and context. This provides the agent with a stable identity, similar to a human employee, ensuring consistent and reliable work.

To get consistent, high-quality results from AI coding assistants, define reusable instructions in dedicated files (e.g., `prd.md`) within your repository. This "agent briefing" file can be referenced in prompts, ensuring all generated assets adhere to a predefined structure and style.

The 'agents.md' file is an open format that functions like a README, but specifically for AI agents. It provides a dedicated, predictable place to store context and instructions, ensuring the AI consistently follows rules for commits, tests, and project setup across all your repositories.

Teams maintain a shared `Claude.md` text file in their Git repo. Anytime the AI errs, they add corrections or context to this file. This acts as a constantly improving, team-wide knowledge base that teaches the AI how to work correctly within their specific project, creating a compounding effect.