AI Skills for Researchers¶
AI skills for researchers encompasses the specific competencies economists and social scientists should develop to effectively use AI tools in their work.
Context & Background¶
The AI skills researchers need are different from those of software engineers or data scientists. Key competencies include:
- Prompt engineering: Writing effective instructions for AI tools
- Output evaluation: Critically assessing AI-generated text, code, and analysis
- Tool selection: Choosing the right AI tool for specific research tasks
- Workflow design: Structuring multi-step AI-assisted processes
- Privacy awareness: Understanding data handling implications of different tools
- Verification methods: Techniques for checking AI output accuracy
Practical Implications¶
- Learn by doing: The best way to develop AI skills is to use AI tools on real research tasks
- Start with your current work: Apply AI to tasks you already understand well
- Join communities: Economics AI user groups share practical knowledge
- Teach others: Teaching AI skills reinforces your own understanding
Key Sources¶
- Your CLAUDE.md
- Workflows
- Chris Blattman Thread: From Claude Code Skeptic to Power User
- Alex Imas Thread: Man-Computer Symbiosis and the Excitement of AI in Research
- Using Claude Code for Tax Season