How to approach source-led research and data analysis
Define the research question, date range, inclusion criteria, and citation format. Provide source files or a source list, and ask for an evidence table that separates direct findings from interpretation.
Example workflows
Organize literature and background sources
Analyze datasets and generate report drafts
Compare public sources and document gaps
A prompt to adapt
Starting brief“Review these five papers for evidence on the stated research question. Create a table with design, population, measure, main finding, and limitation, citing page or section for each entry. Do not combine findings into a causal claim.”
Review before you rely on the result
Check every citation against the original, verify methods and calculations, and follow your institution’s research and AI-use policies.
Get more reliable results from an AI research assistant
Research quality improves when the question and source criteria are clear. Specify the time range, geography, type of evidence, and what counts as reliable; ask the assistant to separate direct evidence from interpretation.
Steps to get a useful result
- Turn the topic into focused questions and inclusion criteria.
- Collect dated primary sources where possible and note disagreements.
- Draft a summary with citations, limitations, and open questions.
Common questions about AI research assistant for source-based research
Can an AI research assistant provide accurate citations?
It can organize citations when it has access to source material, but references can be incomplete or incorrect. Open each source and confirm it supports the statement.
How do I reduce false claims in AI research?
Ask for linked sources and uncertainty notes, prefer primary materials, and verify important facts independently. A confident tone is not evidence.
Related guides
MuleRun features and use-case categories can change. See the official MuleRun use-case directory and product feature overview for current details.