Create Full Literature Review With 30+ Citations In 5 Minutes

AI-assisted literature reviews can be generated in minutes with tightly controlled sources, citation counts, and formatting—then refined into exportable tables, bibliometric charts, and follow-up queries. The workflow centers on AnswerThis, where a user enters a research area (e.g., “atomic structures”) and selects a literature-review mode that determines how comprehensive the output will be and how heavily it leans on back-citations.

After choosing the prompt helper option for a literature review, the interface asks for the research area and then offers model choices. “Full review” is positioned for comprehensive answers with many back citations, while “quick Q&A” targets faster, research-specific questions such as identifying gaps or building outlines. Users can also switch between a base “auto” model and a more advanced “pro mode,” depending on how deep they want the results to go.

The next step is source control. AnswerThis lets users combine multiple repositories—papers plus options including internet search, a library, or uploaded PDFs. For external databases, it can pull from Semantic Scholar, OpenAlex, PubMed, and arXiv, and it can also include patents and items from “my library.” The system supports web search scoping (government/edu pages only or all websites) and claims daily updates to keep results current. Users can further tune retrieval with filters such as a minimum citation threshold, journal quality bands (Q1 highest to Q4 lowest), and publication date windows (start date and optional end date).

Once the search runs, results appear in two parts: a direct answer on the left and the underlying papers on the right. Scrolling through the response reveals citations embedded line-by-line, with a full citation list at the bottom tied to each referenced paper. The draft can then be edited in an AI-assisted word-document interface (“edit with AI”), where users can revise text and insert citations from the selected library, uploaded PDFs, or newly searched papers.

Citation management is built in. Users can switch citation styles on demand—APA, MLA, Chicago, and more—after the draft is generated. Clicking a specific paper surfaces its abstract, supports saving it to the library, and enables “chat with paper” when a PDF is available.

For deeper analysis, AnswerThis includes a research-sources panel that can be expanded into table view. Prebuilt extraction prompts such as “research gaps,” “methodology,” and “future work” populate columns for each source, and users can create custom prompts to extract additional fields. The table can be sorted by date or citation count and exported to CSV or other formats (including exports compatible with tools like Zotero/Mendeley, plus a “big text” option). A “biblometric analysis” feature generates graphs and visuals—citation trends, publication dates, top contributing authors, word clouds, and other charts.

Finally, the system supports iterative research: users can ask follow-up questions that rerun the query to find new papers and update the answer, and they can adjust filters and databases before re-querying. The overall promise is a fast, citation-rich literature review pipeline that moves from drafting to analysis and export without leaving the workflow.