ScholaRAG

Introduction to ScholaRAG

Learn how ScholaRAG transforms the traditional literature review process from weeks of manual work into hours of AI-powered efficiency.

What is ScholaRAG?

ScholaRAG is an open-source, conversational AI-guided system that helps researchers build custom RAG (Retrieval-Augmented Generation) systems for academic literature review. Built on top of VS Code and Claude Code, it guides you through every step of creating a systematic review pipeline.

Key Insight

Unlike generic chatbots, ScholaRAG creates a dedicated knowledge base from your specific research domain, ensuring every answer is grounded in the papers you've screened and approved.

ScholaRAG: The AI Knowledge Flow - Diagram showing the workflow from Academic Papers through PRISMA Filtering to RAG System and AI Assistant

The Problem It Solves

Traditional Literature Review (6-8 weeks)

If you've ever conducted a systematic review, you know the pain:

  1. Database Search: Spend days crafting queries for PubMed, ERIC, Web of Science
  2. Export & Screen: Download 500+ papers, export to Excel, read abstracts one by one
  3. Full-Text Review: Manually review 200+ PDFs for inclusion criteria
  4. Data Extraction: Copy-paste findings, methods, and statistics into spreadsheets
  5. Citation Hell: Constantly re-read papers to verify citations and quotes

The result? 67-75% of your time spent on mechanical tasks instead of analysis.

Common Pain Point

"I've read this paper three times, but I still can't remember which one had the meta-analysis on sample size calculations." — Every PhD student, ever.

With ScholaRAG (2-3 weeks)

  1. 30-minute Setup: Build your RAG system with step-by-step Claude Code guidance
  2. 2-hour Screening: PRISMA pipeline screens thousands of papers automatically
  3. Instant Queries: Ask questions and get answers with specific paper citations
  4. Never Forget: Your RAG system remembers every relevant detail across all papers

Real Results

PhD students using ScholaRAG complete literature reviews in 2-3 weeks instead of 6-8 weeks, spending more time on analysis and writing.

What You'll Build

In approximately 30 minutes of active setup (plus 3-4 hours of automated processing), you'll create:

🔍

PRISMA Pipeline

Screen 500+ papers down to 50-150 highly relevant ones

🗄️

Vector Database

Semantic search using ChromaDB or FAISS

🤖

Research RAG

Query system powered by Claude with citations

Database Strategy

ScholaRAG supports comprehensive multi-database coverage with both free open-access sources and institutional databases.

🌐 Open Access (Free)

Semantic Scholar, OpenAlex, arXiv — 450M+ papers, ~50% PDF access

🏛️ Institutional (Optional)

Scopus, Web of Science — metadata only, 3-5x more papers found

View detailed database strategy →

Core Concepts

1. AI-Powered PRISMA Screening

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is the gold standard. ScholaRAG implements PRISMA 2020 with AI-enhanced multi-dimensional evaluation:

  • Identification: Comprehensive database search with complete retrieval
  • Screening: AI-powered multi-dimensional evaluation using LLMs
  • Eligibility: Confidence-based routing (auto-include/exclude/human-review)
  • Inclusion: Validated final set with optional human agreement metrics

Multi-Dimensional AI Evaluation

ScholaRAG uses AI-PRISMA Rubric with transparent criteria:

  • Sub-criteria scoring - PICO framework evaluation
  • Evidence grounding - AI must quote abstract text
  • Confidence thresholds - Auto-include ≥90%, auto-exclude ≤10%
  • Hallucination detection - Cross-check against abstracts

Achieves 10-20% pass rates matching manual review standards.

2. RAG (Retrieval-Augmented Generation)

RAG combines two powerful capabilities:

  1. Retrieval: Semantic search finds the most relevant papers
  2. Generation: LLM synthesizes answers grounded in retrieved content

3. 7-Stage Workflow

ScholaRAG breaks down the process into 7 conversational stages with Claude Code.

Stage 1-4→Setup & Configuration (~60 min)
Stage 5-7→Execution & Analysis (~3-5 hrs automated)

View detailed 7-stage workflow →

Who Should Use ScholaRAG?

🎓

PhD Students

🔬

Researchers

👨‍🏫

Professors

📚

Librarians

Prerequisites

API Costs

A typical review (500 papers screened, 150 included) costs under $20 with Haiku 4.5 or $25-40 with Sonnet 4.5.

Next Steps

5-Min Quick Start →

Get started instantly with one prompt

Complete Tutorial

Learn the full workflow with real examples

Further Reading: PRISMA Guidelines · Contextual Retrieval

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