Research is one of the most time-consuming parts of Monitoring and Evaluation work.

You may need to find recent evidence, understand an unfamiliar methodology, compare evaluation approaches, verify a statistic, locate relevant reports, review academic literature, investigate a development issue, or identify what other organizations are doing.

Traditionally, this often means opening search engines, reviewing pages one by one, copying information into notes, comparing sources and then trying to build a coherent understanding of the topic.

Perplexity AI offers a different approach.

Instead of simply returning a list of links, Perplexity searches the web and synthesizes information into a conversational answer, while providing citations that allow you to inspect the underlying sources.

For M&E professionals, this makes it particularly useful for research, evidence discovery, fact-checking, topic orientation and comparative analysis.

What You Will Learn

By the end of this tutorial, you will be able to:

• Use Perplexity for M&E research and evidence discovery
• Write better research questions instead of relying on keyword searches
• Use citations to verify AI-generated findings
• Conduct multi-step research without repeatedly starting new searches
• Compare evaluation approaches, tools and methodologies
• Quickly understand unfamiliar M&E topics
• Identify relevant academic, institutional and development-sector sources
• Use Perplexity for fact-checking before writing reports
• Distinguish between research tasks that suit Perplexity and tasks better suited to Google or other tools
• Build a repeatable AI-assisted research workflow
• Apply verification and source-quality controls to AI-assisted research

1. What Is Perplexity AI?

Perplexity is an AI-powered search and research platform.

A traditional search engine generally gives you a collection of links. You then decide which pages to open, read the material and synthesize the information yourself.

Perplexity takes a different approach.

You ask a question in natural language. It searches the web, identifies relevant sources and produces a synthesized response with citations.

The second approach does not eliminate research. It changes where the researcher spends their time.

Instead of spending most of your time finding and assembling information, you can spend more time evaluating whether the information is relevant, credible and appropriate for your work.

Perplexity itself recommends using clear intent and specific questions when searching because more precise requests generally produce better results.

2. Why This Matters for M&E Professionals

M&E work frequently involves questions such as:

These are not simple lookup questions.

They require research across multiple sources.

A useful research assistant therefore needs to do more than find a single answer. It needs to help you explore a topic, identify sources, compare perspectives and continue asking questions.

This is one of the areas where Perplexity can be particularly useful.

3. The First Important Change: Stop Searching Only With Keywords

One of the biggest adjustments when moving from traditional search to AI-assisted search is changing how you formulate your queries.

A traditional search might look like:

A stronger Perplexity query would be:

The second query gives Perplexity:

• the topic
• the task
• the context
• the desired depth
• the source preference
• the timeframe

This produces a much better research starting point.

4. A Simple M&E Research Prompt Formula

For example:

Another example:

This is much more useful than simply typing:

5. Start With Topic Orientation

One of the strongest uses of Perplexity is quickly getting up to speed on a topic you do not know well.

Imagine you have been assigned an evaluation involving climate resilience.

You could spend hours searching for:

• definitions
• frameworks
• indicators
• evaluation methodologies
• recent studies
• measurement approaches
• examples

Instead, begin with:

Do not immediately copy the response into your evaluation report.

Use it as a research map.

Your objective at this stage is to discover:

1. What concepts matter?
2. What terminology is used?
3. Which organizations are publishing relevant work?
4. Which methodologies appear repeatedly?
5. Which sources should I investigate further?
6. Where are the disagreements?

This is much more valuable than simply asking AI to “explain climate resilience.”

6. Use Follow-Up Questions Instead of Starting Again

One of the practical advantages of conversational research is continuity.

Suppose your first question identifies outcome harvesting as a potentially relevant methodology.

Instead of opening another search, continue:

7. Use Perplexity for Evidence Discovery

Perplexity should not be treated simply as an answer generator.

For M&E work, one of its most valuable functions is helping you discover evidence.

Try:

Then ask:

This changes the role of AI.

That is a much stronger research workflow.

8. Learn to Read the Citations

This is one of the most important skills in AI-assisted research.

Perplexity provides citations alongside its answers, allowing you to inspect the original sources.

But a citation does not automatically mean a claim is correct.

A source can be:

• outdated
• low quality
• incorrectly interpreted
• irrelevant to your specific context
• based on a different population
• based on a different methodology
• cited for a claim it does not actually support

9. The Five-Question Source Verification Test

Whenever a Perplexity response gives you an important claim, ask:

This is where professional judgment remains essential.

10. Use Perplexity for Fact-Checking

Before publishing a report, article or presentation, use Perplexity as a verification layer.

For example:

This is much better than asking:

11. Use a Claim-Checking Workflow

The second statement is more defensible because it reflects uncertainty and conditions.

12. Comparing M&E Methodologies

Perplexity is also useful for structured comparisons.

Then follow up:

This allows you to move from:

13. Use Perplexity for Research Gaps

A particularly useful application for evaluators is identifying what is missing from the literature.

This can support:

• evaluation design
• research proposals
• learning agendas
• programme learning
• literature reviews
• evaluation questions
• knowledge products

14. Use Perplexity to Investigate Organizations and Approaches

Suppose you want to understand how international organizations are approaching a particular issue.

This type of research is useful when preparing:

• donor reports
• organizational strategies
• programme proposals
• briefing notes
• evaluation plans
• training materials

15. Perplexity Research Mode for More Complex Questions

Perplexity’s current Research mode is designed for more extensive research tasks. According to Perplexity’s documentation, Research mode performs multiple searches, reads a large number of sources and synthesizes the findings into a comprehensive report.

This is particularly useful when the question is too broad for a normal search.

This is a research task, not a simple lookup.

Use deeper research modes when you need:

• multiple sources
• comparative analysis
• literature exploration
• current evidence
• complex synthesis
• a research briefing

For quick factual questions, a normal search is usually more efficient.

16. When NOT to Use Perplexity

A good AI workflow is not about replacing every tool.

The original experiment found several situations where traditional search remained preferable, including finding a specific known website or page, image search, highly localized searches and obscure searches where a very specific result is needed.

For M&E work, use the right tool for the job.

17. A Practical M&E Research Workflow

Here is a workflow you can reuse.

18. Five High-Value Perplexity Prompts for M&E

19. A More Advanced Prompt: Ask Perplexity to Challenge Your Research

One of the most useful improvements is to stop asking AI only to confirm what you already believe.

Instead, ask it to challenge you.

This creates a much better research process.

That question is extremely valuable in evaluation work.

20. Avoid the “AI Research Bubble”

There is a major risk with AI-assisted research.

You ask a question.

AI gives you an answer.

You ask a follow-up.

It gives another answer.

You eventually develop a convincing understanding of the subject.

But you may still be inside an AI-generated research bubble.

The solution is to deliberately return to the original sources.

21. The M&E Verification Rule

For important professional outputs, use this rule:

High-risk examples include:

• statistics in donor reports
• claims about programme effectiveness
• evaluation findings
• policy statements
• legal or regulatory requirements
• health-related evidence
• financial information
• claims that could materially affect programme decisions

22. Practical Exercise

23. The Research Workflow to Remember

You do not need to remember dozens of prompting techniques.

Remember this:

24. Final Takeaway

Perplexity is most useful for M&E professionals when it is treated neither as a replacement for Google nor as a replacement for professional judgment.

Its real value is in reducing the friction involved in moving from a question to a structured understanding of the available evidence.

It can help you:

• find relevant sources faster
• understand unfamiliar topics
• compare methodologies
• identify evidence
• follow research threads
• fact-check claims
• discover gaps
• build a research map

But the citations are only the beginning of verification.

A strong evaluator should still ask:

That is the difference between using AI to search and using AI to conduct better research.

Quick Reference: Perplexity for M&E

Further Reading

Perplexity’s current documentation describes standard search, Pro Search and Research as different levels of research depth. Research mode is intended for more comprehensive, multi-step research, while Pro Search is designed for more nuanced searches requiring multiple sources.

For M&E professionals, the most effective approach is therefore not to choose one tool permanently, but to develop a research workflow in which AI-assisted search, primary sources, academic literature and professional judgment work together.