How to Use Genspark to Build a Research Report

April 2, 2026 · Editorial Team · 6 min read · genspark research-report agentic-search

Genspark takes a different approach to AI-assisted research than most tools. Instead of giving you an answer and a citation list, it acts more like an autonomous agent: it decides what sub-questions to research, fans out across multiple sources, and assembles a structured output called a Sparkpage. The result lands somewhere between a long-form research brief and a Wikipedia-style overview, except it is built in real time for your specific question.

For building a research report on a topic you do not know deeply, that agentic search behavior is the key feature. You do not have to know what sub-questions to ask. Genspark figures that out for you, then shows you its work.

Starting With the Right Prompt

Type your research topic or question into the search bar. Genspark accepts both keyword-style input ("AI agent market 2025") and natural language questions ("What are the main players and trends in the enterprise AI agent market?"). The natural language version typically produces a better-organized Sparkpage with clearer section headings.

A few prompt patterns that produce better reports:

Include the intended use: "Write a competitive analysis of CRM tools for a mid-market SaaS company." The context shapes what the agent prioritizes.
Specify the depth: "Detailed" or "overview" framing affects how many sub-questions the agent explores.
Mention the audience if relevant: "Suitable for a board presentation" tends to produce more executive-summary-style output.

Do not over-engineer the prompt. A one-sentence question usually works well. The agent will decide what to research.

How the Agentic Search Actually Works

After you submit a query, Genspark does not just run a single search. You can watch it work: a panel on the left shows the sub-queries it is running in real time. Typically it fires 5 to 15 searches on related aspects of your question, reads those pages, and synthesizes them into the Sparkpage structure.

This is the same idea as running 10 Perplexity searches yourself and then stitching together the results, except Genspark does it automatically and produces a single structured document.

The sources appear in a sidebar as they are fetched. You can see the domains being read: news outlets, company websites, industry reports, blog posts, academic papers. The mix varies by topic. For business and technology topics, you will typically see TechCrunch, Crunchbase, company blogs, and some news wires. For scientific topics, it pulls more from journals and preprint servers.

Reading and Editing a Sparkpage

The Sparkpage itself is the output document. It has:

A headline summary at the top
Sections with subheadings organized by theme
Inline citations linked to source URLs
An interactive table of contents on the left

The quality varies by topic. For well-documented topics with lots of online material, Sparkpages are impressively thorough. For niche or very recent topics, the sections can be thin or repetitive.

Here is how I handle a fresh Sparkpage:

Read the headline summary first. If it misunderstands the topic, click the edit button and correct the framing before refining further.
Scan the section headings. Are there obvious gaps? A competitive analysis that does not have a pricing section, for example, is incomplete.
Add sections manually if needed. Genspark allows you to type a heading and run a focused sub-search just for that section.
Check the source list in the sidebar. If the report makes a claim about market size or competitive positioning, find the citation and click through.

The editable structure is what makes Genspark useful for actual deliverables rather than just reading. You can remove sections that do not apply, reorder sections for your audience, and add your own analysis text on top of the generated content.

Mixing Sources Strategically

Genspark pulls from whatever it can find publicly. By default this means a fairly generic web crawl. For research reports where source credibility matters, it is worth intervening in the source mix.

A few tactics:

Pin specific sources: If you want the report to include data from a specific analyst report or company filing you have open, paste the URL directly into the search bar or into a section's sub-search. Genspark will fetch and include it.
Regenerate individual sections: Right-click any section header for an option to re-search that section with a different query. This is useful when a section came back with weak sources.
Compare conflicting sources: If two sections contradict each other (common when a topic has both enthusiast coverage and skeptical coverage), add a section explicitly framing the debate.

The weakest part of Genspark's source mixing is that it cannot access paywalled content. Analyst reports from Gartner or IDC, for instance, will not appear unless there are published excerpts or summaries indexed somewhere publicly. For those data points, you will need to add them manually.

Fact-Checking the Output

The agentic approach produces research faster, but it also compounds errors faster. If one source Genspark read had incorrect data, that data can propagate across multiple sections of the Sparkpage.

My fact-checking process for any Sparkpage I plan to use seriously:

Identify the three to five most important factual claims in the report. These are usually market size numbers, growth rates, competitive rankings, or technology capabilities.
For each, click the inline citation and read the original source.
Check whether the claim in the Sparkpage actually matches what the source says. Specifically watch for: unit mixups (millions vs. billions), date drift (a 2023 forecast presented as a current state), and scope conflation (a statistic about one region applied globally).
For any claim I cannot verify with a source, either remove it or rewrite it as "reportedly" or "according to [source]."

This sounds tedious, but the high-confidence claims usually check out and the suspicious ones surface quickly. In practice it takes 15 to 20 minutes for a mid-length Sparkpage, which is still much faster than building the report from scratch.

Sparkpages can be shared via a public link, exported as a formatted document, or copied section by section. For internal use at a company, the public link option is the most frictionless: send the URL and recipients can read and interact with the Sparkpage directly, clicking citations and reading the source sidebar.

If you need a static document, export to PDF or copy the text into a Google Doc or Word file. The formatting survives reasonably well in copy-paste, though tables may need cleanup.

Realistic Expectations

Genspark produces a solid first draft of a research report in 3 to 5 minutes. That draft needs human review for factual accuracy, and it will not replace a specialist analyst for any topic requiring deep domain judgment.

Where it genuinely saves time is in the initial synthesis pass: instead of spending 90 minutes reading articles to understand a new topic, you spend 90 minutes reviewing a structured draft and improving it. That is a meaningful difference for any researcher or analyst who regularly needs to get up to speed on unfamiliar subjects.

The agentic search is also genuinely good at surfacing sources you would not have found with a manual keyword search. I have consistently found Sparkpages pointing to relevant blog posts, case studies, and reports that I did not know existed.