People struggle with AI prompts because the model lacks background on their goals and progress. The solution is 'Context Engineering': creating an environment where the AI continuously accumulates user-specific information, materials, and intent, reducing the need for constant prompt tweaking.

Use an AI assistant like Claude Code to create a persistent corporate memory. Instruct it to save valuable artifacts like customer quotes, analyses, and complex SQL queries into a dedicated Git repository. This makes critical, unstructured information easily searchable and reusable for future AI-driven tasks.

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.

Don't try to create a comprehensive "memory" for your AI in one sitting. Instead, adopt a simple rule: whenever you find yourself explaining context to the AI, stop and immediately have it capture that information in a permanent context file. This makes personalization far more manageable.

Instead of using siloed note-taking apps, structure all your knowledge—code, writing, proposals, notes—into a single GitHub monorepo. This creates a unified, context-rich environment that any AI coding assistant can access. This approach avoids vendor lock-in and provides the AI with a comprehensive "second brain" to work from.

Instead of a complex database, store content for personal AI tools as simple Markdown files within the code repository. This makes information, like research notes, easily renderable in a web UI and directly accessible by AI agents for queries, simplifying development and data management for N-of-1 applications.

Most users re-explain their role and situation in every new AI conversation. A more advanced approach is to build a dedicated professional context document and a system for capturing prompts and notes. This turns AI from a stateless tool into a stateful partner that understands your specific needs.

Long-running AI agent conversations degrade in quality as the context window fills. The best engineers combat this with "intentional compaction": they direct the agent to summarize its progress into a clean markdown file, then start a fresh session using that summary as the new, clean input. This is like rebooting the agent's short-term memory.