Unlocking the Power of LLMs: Lessons from a Super-User
With the rising hype around Claude Code and AI agents, practical insights are essential. After months of hands-on experience with Cursor and Claude Code, I’ve distilled key takeaways for maximizing AI in coding.
Key Insights:
- Quality Matters: Claude Code falls short compared to Cursor, leading to messy codebases. Productivity can plummet if not careful.
- Understand Limitations: LLMs don’t “think.” They require precise inputs; garbage in means garbage out.
- Context is King:
- Provide 99% of relevant code in context.
- Refactor code for better AI processing—modular and clean is the way.
- Control the AI: Limit tasks to small, trackable pieces to avoid a chaotic output.
Think of LLMs as powerful tools that need strict boundaries for effective control.
