The Deep Research Stack

Mine papers, verify claims, and map literature with AI

This workflow transforms a raw research question into a mapped, verified literature landscape. You start by scoping the field with broad web and citation-aware answers (Perplexity), then refine to specific papers (Elicit), validate findings with consensus evidence (Consensus), deep-dive into selected papers (SciSpace), and finally get TLDR summaries for quick scanning (Semantic Scholar). This combination works because each tool covers a different gap: Perplexity gives context and reliable citations, Elicit filters for methodological quality, Consensus checks scientific consensus, SciSpace unpacks the details, and Semantic Scholar offers one-stop metadata. It's for graduate students, scientists, and analysts who need a thorough, evidence-based literature review without reading every paper in full.

The workflow, step by step

1
Scope the research landscape
Use Perplexity's deep research mode to get a broad overview with cited sources. It's better than a general browser because it curates reputable web and academic results.
Hand-off → A list of key concepts, key papers, and initial references.
2
Find relevant papers systematically
Elicit
Elicit is built for literature search; it can filter by study design and extract data. It's more targeted than Perplexity for academic papers.
Hand-off → A curated list of relevant papers with summaries and extraction tables.
3
Validate key claims against evidence
Consensus
Consensus uses GPT-4 on scientific papers to answer yes/no questions backed by evidence. It helps check whether the claims you've found are supported by peer-reviewed literature.
Hand-off → A set of verified or disputed claims with supporting citations.
4
Deep-dive into selected papers
SciSpace
SciSpace lets you chat with PDFs, ask questions, and get explanations. It's ideal for understanding complex methods or results in the papers you prioritized.
Hand-off → Detailed notes and clarifications from each paper you analyzed.
5
Summarize and map the literature
Semantic Scholar
Semantic Scholar provides TLDR summaries for millions of papers, plus citation graphs and author networks. It's a quick way to get a bird's-eye view.
You end with: You have a comprehensive research brief: scoped topic, curated paper list, verified claims, paper notes, and a summary map.

Free

Frequently asked questions

Is this entire stack free?

Mostly. Perplexity's deep research requires Pro ($20/mo), Consensus uses free credits, Elicit has a free tier with limits, SciSpace has free usage, Semantic Scholar is free. Total possible free, but heavy use may need subscription.

Can I replace any tool with a free alternative?

Yes. For Perplexity you can use Google Scholar; for Elicit, you can use Semantic Scholar's search. But the workflow is optimized for the strengths of each.

Where should I start?

Start with a clear, focused research question. Use Perplexity to frame the landscape, then proceed stepwise; don't skip steps.

What is a common mistake?

Jumping to deep reading too early. Resist opening full PDFs until after Consensus verification, so you prioritize papers with real consensus.

What if I have no budget?

Focus on free tiers: use Semantic Scholar as first step instead of Perplexity, then manually do what Elicit and Consensus offer. But the workflow quality drops.