Week 2 · Adaptive Planning Program

7. Generative AI for Literature & Policy Scanning

30 min

By the end you'll be able to

Construct effective prompts for AI-assisted literature review
Validate AI-generated outputs against primary sources
Apply ethical principles to AI use in public health contexts

The volume of public health literature is overwhelming, but AI can be a powerful partner in synthesis. This lesson teaches Prompt Engineering specifically for public health, demonstrating how to use large language models to summarize community assessments and extract policy themes.

We emphasize a “Human-in-the-Loop” approach to validate AI outputs and discuss the critical ethical considerations regarding data privacy and algorithmic transparency.

Practice quiz

Question 1
What is ‘Prompt Engineering’ as the lesson uses the term?
- A coding standard for backend AI infrastructure.
- The disciplined craft of writing instructions to large language models so they reliably produce useful public health outputs.
- A regulatory framework requiring disclosure of AI use in grant applications.
- A user interface design pattern for chat applications.
Question 2
Which approach does the lesson prescribe for using AI outputs in literature and policy scanning?
- Auto-publish the AI summary directly to stakeholders to save time.
- Use AI only when no human expert is available.
- A ‘Human-in-the-Loop’ approach that validates AI outputs and weighs ethical issues like privacy and algorithmic transparency.
- Replace the literature review entirely with a single LLM query.
Reflection 3
Name one ethical risk the lesson flags for using generative AI in public health work, and describe a concrete safeguard.

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