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.

To maximize an AI assistant's effectiveness, pair it with a persistent knowledge store like Obsidian. By feeding past research outputs back into Claude as markdown files, the user creates a virtuous cycle of compounding knowledge, allowing the AI to reference and build upon previous conclusions for new tasks.

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 spending time trying to craft the perfect prompt from scratch, provide a basic one and then ask the AI a simple follow-up: "What do you need from me to improve this prompt?" The AI will then list the specific context and details it requires, turning prompt engineering into a simple Q&A session.

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.

Instead of manually rereading notes to regain context after a break, instruct a context-aware AI to summarize your own recent progress. This acts as a personalized briefing, dramatically reducing the friction of re-engaging with complex, multi-day projects like coding or writing.

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.

Instead of guessing where AI can help, use AI itself as a consultant. Detail your daily workflows, tasks, and existing tools in a prompt, and ask it to generate an "opportunity map." This meta-approach lets AI identify the highest-impact areas for its own implementation.