Prompt Engineering: Unlocking the Full Power of AI

Large language models (LLMs) are increasingly shaping research, policy analysis, and media work. Their effectiveness depends on how interaction with them is structured. Prompt engineering provides a methodological framework for producing reliable, analytically useful outputs across both GUI environments and API integrations.

This lecture equips participants with the ability to:

• Understand how LLMs process instructions, tokens, and context windows

• Design structured prompts for research, analytical, and policy-oriented tasks

• Apply various techniques to improve depth and precision

• Use role definition, constraints, and stepwise reasoning to enhance output quality

• Identify bias, hallucinations, and uncertainty in model responses

• Evaluate and validate AI-generated content for academic and professional use

• The session frames prompting as a disciplined approach to human–AI interaction, with emphasis on reproducibility, epistemic reliability, and analytical accountability in politically sensitive and information-dense contexts.