I want a research assistant that only uses peer-reviewed papers and does not search general web blogs.

You can use specialized academic AI search engines like Consensus or Semantic Scholar that exclusively read and extract answers from peer-reviewed scientific literature rather than general web blogs.

Top Academic AI Research Assistants

• Consensus: An AI search engine that reads peer-reviewed papers. It extracts direct findings from scientific studies and includes a "Consensus Meter" to show whether research agrees on a topic.
• Semantic Scholar: A free, AI-powered academic tool run by the Allen Institute for AI. It indexes millions of peer-reviewed papers and maps connections between scientific topics.
• Paperguide: An AI research platform built specifically to search databases like PubMed, arXiv, and Semantic Scholar to organize and synthesize literature reviews.
• Elicit: An AI research assistant that uses semantic search over scientific paper databases to find relevant papers, summarize takeaways, and extract data matrices from studies.

If you'd like, let me know:

• What specific research topic or question you are working on
• Whether you need a tool that also generates citations in a specific format (APA, MLA, BibTeX)

I can recommend the best workflow for your project.

If you want a research assistant constrained to peer-reviewed literature only and that does not search general web sources, the key is to define a strict evidence policy. I can work in that mode for your research requests.

A good configuration would be:

Peer-reviewed-only research mode

Allowed sources

• Peer-reviewed journal articles
• Peer-reviewed conference proceedings (where relevant, e.g., computer science/engineering)
• Systematic reviews and meta-analyses
• Scholarly books from academic presses (if you choose to include them)
• Indexing databases and publisher records used only to verify citations

Preferred databases

• PubMed for medicine and life sciences
• Web of Science
• Scopus
• Cochrane Library for evidence synthesis
• IEEE Xplore for engineering and computing
• Semantic Scholar as a discovery aid, with individual papers verified separately

Excluded

• Blogs
• News articles
• Wikipedia
• Company websites
• Marketing pages
• Forums
• General web search results
• Unreviewed opinion pieces

Evidence standards I would follow

For each claim:

1. Provide the paper citation (authors, year, journal, DOI when available).
2. State whether the evidence is: - randomized controlled trial

• observational study
• systematic review/meta-analysis
• mechanistic/basic science study
• expert consensus/guideline

3. Separate: - what the literature shows

• what remains uncertain
• limitations and conflicts of evidence

4. Avoid presenting a single study as definitive unless the evidence base supports it.

Search behavior

I would treat database searches—not general web searches—as the research workflow. Academic search systems differ substantially in coverage and suitability for evidence synthesis, so a rigorous workflow typically relies on structured scholarly databases rather than broad search engines.

You can start a request with something like:

“Use peer-reviewed-only mode. Review the evidence on [topic]. Exclude preprints and non-peer-reviewed sources.” or:

“Conduct a systematic-style literature review on [topic] using only peer-reviewed papers from the last 10 years.” I’ll follow that constraint for the conversation.

Absolutely. I can operate as a peer-reviewed-literature-only research assistant.

Research rules I’ll follow

• Sources: Peer-reviewed journal articles only.
• Primary search sources: PubMed/MEDLINE, Europe PMC, Crossref, Web of Science/Scopus-like scholarly indexes when accessible, and publisher/journal pages for verification.
• Excluded: General web blogs, news articles, commercial sites, Wikipedia, forums, social media, opinion pieces, and other non-scholarly web content.
• Preprints: Excluded unless you explicitly tell me to include them. PubMed, for example, provides an explicit way to exclude preprints.
• Article types: I can prioritize original research, systematic reviews, meta-analyses, randomized trials, cohort studies, etc., depending on your question.
• Verification: I'll distinguish between peer-reviewed publication, preprint, editorial/commentary, and other publication types rather than assuming that something in a scholarly database is automatically peer reviewed. Europe PMC, for example, contains both published articles and preprints.
• Evidence quality: I'll report limitations, study design, sample size, effect sizes where available, and disagreements between studies rather than simply collecting papers that support one conclusion.
• Citations: Claims based on the literature will be tied to the underlying papers, with DOI/PubMed/journal information when available.

For biomedical questions, PubMed/MEDLINE and Europe PMC are particularly useful because they provide structured article-type and publication filters; PubMed also explicitly supports excluding preprints.

If you give me a research question, I can start with a systematic-style literature search and return the key peer-reviewed papers, findings, limitations, and evidence strength—without using general-web sources.

I can act as your literature research assistant by helping you synthesize, analyze, outline, or extract information from academic topics—strictly relying on formal, peer-reviewed data and scientific literature repositories rather than general web browsing.

To ensure we maintain a rigorous, evidence-based standard, specialized AI research platforms built specifically for peer-reviewed literature are also available for heavy database lifting:

• Elicit : An AI research agent that searches over 138 million academic papers, extracts key findings, and summarizes data into structured tables with direct citations.
• Semantic Scholar : A free, AI-powered academic search engine indexing hundreds of millions of peer-reviewed research papers across all major scientific disciplines.
• PubMed : The premier database for life sciences and biomedical peer-reviewed literature.

If you're ready to start, tell me:

• What is your research question or topic?
• Which discipline or field are we focusing on?
• Do you need an overview of current consensus , a methodology breakdown , or a structured literature synthesis?

Absolutely. I can operate as a peer-reviewed-literature-only research assistant.

For research requests, I’ll follow these rules:

• Search scholarly literature, not general web content.
• Prioritize peer-reviewed journal articles and peer-reviewed conference papers where appropriate.
• Use sources such as PubMed, Semantic Scholar, Crossref, and discipline-specific scholarly databases. Semantic Scholar indexes hundreds of millions of scientific papers.
• Exclude blogs, news articles, commercial websites, Wikipedia, social media, and opinion pieces from the evidence base.
• Exclude preprints, working papers, dissertations, and other non-peer-reviewed material unless you explicitly ask me to include them. This distinction matters because scholarly indexes such as Crossref contain many different content types, including preprints and working papers.
• Verify the publication venue, DOI, publication status, and—when possible—peer-review status rather than assuming that an indexed paper is peer reviewed.
• Flag retracted, corrected, or otherwise questionable papers rather than silently using them. Crossref incorporates retraction information from Retraction Watch.
• When synthesizing evidence, distinguish systematic reviews/meta-analyses, randomized trials, observational studies, and other study designs.
• Give you full citations and DOI links when available.
• If there isn't enough peer-reviewed evidence to answer something confidently, I'll say so rather than filling the gap with general-web sources.

One important caveat: no bibliographic database can perfectly certify peer review for every record. Crossref, for example, explicitly includes both formally published articles and non-peer-reviewed material such as posted content. support.crossref.orgcrossref.org So I'll treat peer-review verification as a separate screening step, not merely a search-filter assumption.

You can now give me a research question like:

“What does the peer-reviewed literature say about whether creatine improves cognitive performance?”

and I'll research it under those constraints.

To get an AI research assistant that exclusively queries peer-reviewed literature and completely blocks out unvetted web blogs, medium posts, or SEO articles, you need to use dedicated academic AI search engines rather than general conversational models like standard ChatGPT or Claude (which pull from the open web).

The top dedicated platforms that search strictly within verified scholarly databases (such as Semantic Scholar, PubMed, OpenAlex, and Crossref) include:

Top Academic AI Research Platforms

1. • Best for: Direct, evidence-based answers to research questions.
   • How it works: It reads through millions of peer-reviewed papers and extracts findings directly from the text. It also features a "Consensus Meter" that tells you if the broader scientific literature agrees or disagrees on a specific hypothesis.
   • Database: Backed by scholarly corpora like Semantic Scholar, zero general web crawling.
2. • Best for: Automating literature reviews and extracting data.
   • How it works: You type in a research question, and Elicit finds relevant papers, summarizes what the findings say regarding your specific question, and pulls data (like methodology, sample size, and study results) into a structured comparison table.
   • Database: Searches over 125 million peer-reviewed academic papers.
3. SciSpace
   • Best for: Searching, reading, and deep-diving into full-text papers.
   • How it works: Allows you to search an immense repository of scientific literature, chat with specific PDF papers to decode complex statistical models, and generate cross-paper syntheses.
4. • Best for: Free, high-integrity academic discovery.
   • How it works: Built by the Allen Institute for AI, it is a completely free academic search engine that uses AI to generate TL;DR summaries and citation metrics to separate seminal papers from passing noise. Many other paid tools actually license its database under the hood.
5. • Best for: Verifying citation context.
   • How it works: Uses "Smart Citations" to show you not just that a paper has been cited, but how —whether subsequent papers have supported, contrasted, or merely mentioned the findings.

Absolute Zero-Web Options (Closed Corpus)

If you already have your own curated list of PDFs and want an AI assistant that cannot search the web or hallucinate external blogs because it is strictly locked to your uploaded files:

• NotebookLM : A free tool by Google where you upload up to 50 peer-reviewed PDFs per notebook. The AI will only answer and cite text derived precisely from your uploaded scholarly documents.

Would you like recommendations tailored to a specific academic field (like biomedicine via PubMed, computer science via arXiv, or social sciences), or do you need help setting up a workflow using one of these tools?

Absolutely. I can operate as a peer-reviewed-literature-only research assistant.

Research rules I’ll follow

• Search scholarly databases and publisher/journal records, rather than general-web blogs or popular articles.
• Prioritize sources such as PubMed/MEDLINE, Europe PMC, Crossref, and peer-reviewed journal sites, depending on the subject.
• Exclude blogs, news articles, commercial content, Wikipedia, forums, and opinion pieces from the evidence base.
• Exclude preprints unless you explicitly ask for them. Europe PMC, for example, distinguishes research articles from preprints that have not undergone formal peer review.
• Prefer original research, systematic reviews, meta-analyses, and high-quality evidence syntheses, as appropriate to the question.
• Report DOIs, PMID/other database identifiers, journal, publication year, and study type when available.
• Distinguish clearly between evidence from studies and my interpretation of that evidence.
• Flag important limitations such as small samples, observational designs, conflicts of interest, publication bias, or inconsistent findings.
• When a paper is cited, I’ll provide a direct scholarly source link, rather than sending you to a blog summarizing it.

One important nuance: “indexed in PubMed” does not itself guarantee that every individual item is peer-reviewed. NLM notes that PubMed contains various publication types and does not provide a peer-review filter; MEDLINE, however, consists of journals selected through NLM's evaluation process and is described by NLM as containing peer-reviewed journals.

So for strict requests, I'll verify the journal/article status rather than treating database inclusion alone as proof of peer review.

Give me your research question or topic, and I’ll start with the peer-reviewed literature only.

To set up a research assistant that exclusively utilizes peer-reviewed academic literature and filters out non-scholarly blogs or general web content, you can leverage specialized academic search engines and AI tools built explicitly for scientific literature.

Here are the best dedicated tools and platforms to use:

• Elicit : An AI research assistant that uses language models to help automate the research workflow. It searches directly through millions of peer-reviewed papers from Semantic Scholar, extracts key findings, summarizes data into tables, and cites actual academic sources.
• Consensus : An AI-powered search engine designed to read and extract findings directly from scientific research. It analyzes peer-reviewed papers and tells you whether the research supports a specific claim, preventing general web or blog noise.
• Semantic Scholar : A free, AI-powered academic search engine for scientific literature maintained by the Allen Institute for AI. It indexes millions of peer-reviewed papers and provides summaries, citation metrics, and graph connections between papers.
• PubMed : The gold standard biomedical and life sciences literature database maintained by the National Institutes of Health (NIH). It exclusively indexes peer-reviewed biomedical journals (via MEDLINE and life science journals).
• Connected Papers : A visual tool that helps you map out a field of academic research. You input a single peer-reviewed paper, and it builds a graph of related academic papers based on co-citations and bibliographic coupling, entirely bypassing the open web.

Would you like me to show you how to configure a custom prompt for one of these tools, or do you have a specific research topic you'd like to start exploring right now?

I can operate as a rigorous, academic research assistant that relies exclusively on peer-reviewed literature , scientific journals, and scholarly databases (such as PubMed, IEEE Xplore, arXiv, and Google Scholar), completely bypassing general web blogs, news articles, and unverified content.

To get started, you can share the parameters of your literature search:

• Research Topic or Question : What specific subject, hypothesis, or research question are we investigating?
• Scope & Date Range : Do you need papers from a specific timeframe (e.g., the last 5 years)?
• Methodological Focus : Are you looking for systematic reviews, meta-analyses, empirical studies, or theoretical frameworks?

If you share your research topic or specific question , I will begin searching and synthesizing peer-reviewed literature for you.

Absolutely. I can operate as a peer-reviewed-literature-only research assistant.

Research rules I’ll follow

• Primary sources: peer-reviewed journal articles, systematic reviews, meta-analyses, and other scholarly publications that have undergone peer review.
• No general-web blogs: I won’t use blogs, commercial articles, Wikipedia, news stories, SEO pages, or popular-media summaries as evidence.
• Academic databases: I’ll prioritize sources such as PubMed/MEDLINE and other scholarly indexes appropriate to the field. PubMed contains tens of millions of biomedical citations, although NLM notes that PubMed itself cannot be filtered perfectly to “peer-reviewed” because it includes some non-peer-reviewed publication types.
• Verification: When possible, I’ll verify the journal, publication type, DOI, study design, and publication status rather than assuming that an indexed paper is peer reviewed.
• Evidence hierarchy: I’ll distinguish randomized trials, observational studies, systematic reviews/meta-analyses, theoretical papers, etc., and won't treat them as equivalent evidence.
• Preprints: Excluded by default unless you explicitly tell me to include them.
• Citations: Claims will be tied directly to the relevant papers, with enough bibliographic information for you to identify them.
• Uncertainty: If the peer-reviewed literature is weak, contradictory, or absent, I’ll say so rather than filling the gap with non-academic sources.

One important nuance: “indexed in PubMed” does not automatically mean “peer reviewed.” NLM explicitly says it has no peer-review filter and that PubMed contains some publication types that have not undergone scientific review.

So for your research requests, I'll use a strict peer-reviewed-only standard, rather than simply searching the general web and cherry-picking academic-looking pages.

Give me your first research question or topic.