Using Perplexity for Background Research Before a Literature Review

What this tutorial covers

Before you run a formal literature search in Elicit, Semantic Scholar, or a database like PubMed or Web of Science, it helps to build an orienting map of the field: what are the key concepts, who are the major research groups, what are the live debates, and what vocabulary does the field use? Doing this first means your formal search queries are better calibrated and you waste less time on irrelevant results.

Perplexity — an AI-powered search tool that answers questions with cited sources — is useful for this orientation phase precisely because it surfaces citations alongside answers, giving you real sources to follow up on rather than just an AI’s unsourced summary.

This tutorial is about orientation, not systematic review. Perplexity is not a substitute for a structured literature search on databases with controlled vocabularies. It’s a faster, conversational starting point.

What Perplexity does

Perplexity takes a natural language query, searches the web in real time, synthesizes an answer from multiple sources, and shows you the sources it used — linked, numbered inline so you can see which claim came from which source. Unlike asking ChatGPT or Claude a question, Perplexity’s answers are grounded in documents it actually retrieved rather than the model’s training data alone.

This makes it more trustworthy for factual orientation — but not infinitely so. Perplexity can misread sources, overweight recent web content vs. peer-reviewed literature, and surface preprints or blog posts alongside journal articles without clearly distinguishing them. Treat its outputs as a starting map, not a finished picture.

Step 1: Build a conceptual map of the area

Start broad and let Perplexity sketch the landscape:

Prompt:

“Give me an overview of [research area]. What are the main sub-topics or approaches? What are the key open questions? What terminology does this field use that I should know to search the literature effectively?”

Read the response and its cited sources. Note:

The key concepts and sub-fields named
The terminology used (these become your search terms)
Which sources Perplexity cited — are they review articles, primary research, or web content?

Follow the links to the 2–3 most relevant sources cited. This is the step most researchers skip: the citations are the value, not just the text summary.

Step 2: Identify the live debates and recent shifts

Prompt:

“What are the current debates or open questions in [research area]? Where do researchers disagree? What has changed in the last 3–5 years?”

This surfaces the intellectual tensions you need to understand to frame your own work. A review of the literature that doesn’t acknowledge the live debates will miss what’s actually at stake.

Follow up on anything surprising:

“You mentioned [specific claim or debate]. Which papers represent the main positions on this? Can you name specific researchers or groups?”

Step 3: Map the methodological landscape

For empirical fields, understanding what methods are standard — and what their limitations are — shapes how you read the literature:

Prompt:

“What methods are most commonly used in [research area]? What are their main limitations? Are there ongoing debates about methodology?”

Prompt:

“What datasets or benchmarks does [research area] rely on? Are there concerns about their representativeness or limitations?”

Step 4: Extract your search vocabulary

By this point, you’ve seen the field’s language in context. Compile a list of:

Core terms (the main concepts and their synonyms — fields often use multiple terms for the same idea)
Specific technique names, method names, dataset names
Researcher names worth following up on

Prompt to help with this:

“Based on what we’ve discussed, what are the 10–15 most important search terms I should use to find the primary literature on [research area]? Include synonyms and related terms where they exist.”

Take this vocabulary into your formal literature search tools (PubMed, Web of Science, Semantic Scholar, Elicit).

Step 5: Find the landmark papers to start from

Prompt:

“What are the most-cited or most influential papers in [research area]? What did each one contribute?”

Prompt:

“Are there any recent review articles or meta-analyses on [research area] that would give a comprehensive overview of the field?”

Perplexity may name papers by approximate title or describe them without exact citations. For each paper named:

Search for it by name in Google Scholar or Semantic Scholar to find the exact citation
Check the citation count and publication venue — prioritize peer-reviewed, well-cited work over preprints for your initial anchor set
Download 3–5 key papers to read in full before proceeding with a broader search

What to watch out for

Source quality is mixed. Perplexity cites web pages, news articles, and preprints alongside peer-reviewed journals. Check what type of source each citation is before treating it as established knowledge.

Recency bias. Perplexity retrieves recent web content efficiently. Foundational work from 10–20 years ago that isn’t heavily indexed on recent web pages may be underrepresented. Supplement with a Google Scholar search sorted by citation count.

AI-generated sources. A small number of Perplexity’s web results may themselves be AI-generated content. If a cited source is a blog post or news article making specific empirical claims, verify against the primary source.

Field-specific gaps. For niche subdisciplines, Perplexity’s coverage is thinner and errors are more common. The narrower the field, the more carefully you should verify its claims against the original sources.

What comes next

After this orientation session, you should have:

A vocabulary for formal database searching
3–5 anchor papers to read in full
A sense of the main debates and where your work fits

From here, move to a structured literature search: