How to Migrate From Consensus to Elicit
Researchers who move from Consensus to Elicit are usually at a specific inflection point in their work: they've graduated from "find papers that answer my question" to "analyze what a body of literature says across dozens or hundreds of papers." Consensus is excellent at the first task, you ask a research question and it surfaces studies with consensus indicators and quality ratings. Elicit is built for the second task, structured extraction, comparison, and synthesis across a large paper set.
The other major trigger for this switch is systematic review work. If you're conducting a literature review where you need to track specific data points (sample sizes, effect sizes, intervention types, study populations) across every included study, Consensus doesn't give you that infrastructure. Elicit's column-based extraction model was designed specifically for this workflow, and it saves hours that would otherwise go to manual spreadsheet entry.
What's actually different
The conceptual difference is querying versus analyzing. Consensus is a search interface over scientific literature with AI-powered relevance ranking and consensus indicators. Elicit is a research workflow tool that treats papers as structured data, extractable, comparable, and filterable at scale.
| Feature | Consensus | Elicit |
|---|---|---|
| Core use case | Question answering from papers | Systematic extraction and synthesis |
| Citation source | Primarily Semantic Scholar | Semantic Scholar + broader indexing |
| Column extraction | No | Yes, extract any field across papers |
| Study type filtering | Yes (RCT, meta-analysis, etc.) | Yes |
| Export to spreadsheet | Basic | Structured CSV with extracted columns |
| Collaborative review | Limited | Yes, shared workflows |
| Best for | Quick literature search | Systematic reviews, structured analysis |
| Pricing | Free / Pro | Free / Pro |
The extraction feature deserves more explanation because it's the real differentiator. In Elicit, once you have a set of papers, you can add columns to a table and ask Elicit to extract specific information from each paper automatically: "What was the sample size?", "What was the primary outcome measure?", "Was the control group randomized?". It fills these columns across all papers at once, giving you a structured dataset from an unstructured literature pile in minutes rather than hours.
Mapping your existing workflow
If you used Consensus primarily for question-mode queries ("Is intermittent fasting effective for weight loss?"), that workflow translates directly to Elicit's question input. Run the same query and you'll get a similar set of papers with summaries. The interface looks different but the core search is comparable.
Where the mapping gets more intentional: Consensus's consensus meter (the percentage of papers agreeing with a claim) doesn't have a direct equivalent in Elicit. Elicit gives you the data to form your own synthesis rather than a pre-computed score. Whether that's an improvement depends on whether you trust your own judgment more than an aggregate, for systematic work, you'll want to draw your own conclusions from extracted data anyway.
For any workflow where you previously exported Consensus results to a spreadsheet and then manually read papers to fill in additional columns, that entire manual step can now happen inside Elicit. Your new workflow: search in Elicit, select relevant papers, define your extraction columns, export a completed table.
Consensus's Copilot (the AI chat layer for asking questions about a set of papers) maps to Elicit's ask-about-papers functionality. You can pose synthesis questions about your paper set in both tools.
The actual migration steps
1. Export your Consensus history if you have saved searches. Consensus lets you save searches and papers. Copy the relevant paper lists, DOIs, titles, or full citations, before you switch. Elicit can import papers by DOI or title.
2. Create an Elicit account. Elicit's free tier allows a meaningful amount of extraction work, but the column limit increases significantly with Pro. If you're running a full systematic review, the Pro tier is worth it.
3. Start a new notebook in Elicit. Elicit organizes work into notebooks (projects). Create one for your current research question. This is the rough equivalent of a saved search in Consensus, but with more structure.
4. Run your research question as a search. Type your question in natural language. Elicit returns papers with AI-generated summaries of how each paper is relevant. Review the top results and add relevant ones to your notebook.
5. Define your extraction columns. This is where Elicit's value materializes. Click "Add column," type the field you want to extract (e.g., "sample size," "intervention type," "outcome measured"), and Elicit auto-fills it for every paper in your set. Start with the four or five columns that would have taken the most manual reading time in your old workflow.
6. Export and verify. Export to CSV. Spot-check five or six cells against the actual papers. Elicit's extraction accuracy is good but not perfect, verification is still your job, especially for numerical data where precision matters.
Gotchas you'll hit
Extraction is probabilistic. Elicit reads PDFs and extracts values using an AI model. For clearly stated numerical data (sample size stated as "n = 240"), accuracy is high. For derived or implicit information ("is this study adequately powered?"), you'll get an interpretation, not a fact. Treat extracted data as a starting point for verification, not a final answer.
Paper coverage isn't identical. Both tools use Semantic Scholar as a primary source, but results for narrow or niche topics can vary. If Consensus reliably surfaced a specific paper for your query and Elicit doesn't, try adding it manually by DOI.
No consensus indicator. Researchers who found Consensus's summary "X% of papers agree" valuable will need to synthesize that themselves from Elicit's extracted data. It's more work but more transparent.
Learning curve on column design. Getting good extractions requires asking the right column question. "What is the effect?" is too vague; "What is the reported effect size (Cohen's d or equivalent)?" is better. Spend a few minutes refining column prompts before running them at scale.
Free tier limits. Elicit's free tier caps the number of papers and columns per notebook. A full systematic review with 50+ papers and 10+ columns will require Pro. Budget for this before starting a large project.
When NOT to switch
Stay with Consensus if your research workflow is primarily exploratory, you want to quickly gauge what the literature says about a topic before deciding whether to go deeper. Consensus's consensus indicator and clean question-answer format are faster for that initial assessment than Elicit's notebook-and-column approach.
Consensus is also better for sharing results with a general audience. Its outputs are more immediately readable to people who aren't researchers. Elicit's tables are for analysis, not communication.
If you're not doing systematic work that requires cross-paper comparison, the overhead of setting up Elicit notebooks and columns adds friction without proportional benefit.
The switch from Consensus to Elicit pays off at scale. For a question with 10 relevant papers and three things you want to know about each, Elicit saves you a few hours. For a systematic review with 80 included studies and a 15-column extraction table, it saves you a week. The more papers and data points your research involves, the stronger the case for making the move.