top of page
Search

Build a Corrective RAG Agent to Fact-Check News Articles

In an era of misinformation, the ability to automatically fact-check news content using AI is becoming essential. With the rise of generative AI and the viral spread of false information, 2025 is the perfect time to build a Corrective RAG Agent—a cutting-edge project that combines Retrieval-Augmented Generation (RAG) with multi-agent collaborationto identify and correct inaccuracies in online articles.


This hands-on project blends natural language processing, semantic search, and automated reporting, making it ideal for aspiring AI engineers, data journalists, or startups focused on responsible media tech.


Build a Corrective RAG Agent to Fact-Check News Articles



🎯 Project Overview


Objective:

Create a multi-agent AI system that reads a news article, verifies each factual claim against trusted sources, and produces a detailed report with flagged inaccuracies and AI-generated corrections.



🌍 Why It’s Relevant in 2025

  • Generative AI is being misused to create misleading content

  • AI-powered journalism is gaining traction (e.g., Google’s Genesis, Reuters AI Fact Check)

  • Tech platforms are investing in real-time misinformation detection

  • This project combines the trending trio: LLMs + RAG + AI Agents



🛠️ Tools & Tech Stack

  • Language Model: Llama 3

  • Embedding Model: Sentence Transformers (all-MiniLM, multi-qa-MiniLM)

  • Vector Database: FAISS

  • Agent Framework: LangChain or CrewAI

  • Programming Language: Python 3.8+



📚 Data Requirements

  • Topic: Choose a theme such as climate change, healthcare, or tech policy

  • Trusted Sources: Gather 10 high-quality articles from sites like:

    • NASA, WHO, IPCC, BBC, The Guardian

    • Convert them into clean, chunked text for embedding



⚙️ Workflow Breakdown

Step 1: Article Input

  • Provide a 500-word news article (real or user-written) on the selected topic.

Step 2: Fact-Checker Agent

  • For each claim in the article, the agent:

    • Retrieves relevant content from the trusted dataset via FAISS

    • Uses semantic similarity scoring to rank evidence

Step 3: Corrector Agent

  • Highlights discrepancies between the article and trusted sources

  • Suggests accurate, fact-based revisions

Step 4: Output Report

  • Two sections:

    • 🧾 Original article (unchanged)

    • 🛠 Fact-checking report with:

      • Flagged inaccuracies

      • Correction suggestions

      • Supporting sources



✅ Deliverables

  • A Python script implementing both agents

  • A .txt or .md file containing:

    • Original article

    • Fact-checking report (clearly formatted)



📏 Evaluation Criteria

Metric

Description

🔍 Inaccuracy Detection

Number and quality of false claims identified

📚 Correction Accuracy

Corrections match facts from trusted sources

✍️ Report Clarity

Easy-to-read, properly structured and sourced


Step-by-Step Guide to Build Your Fact-Checking Agent

Let’s break down this project into actionable steps so you can build your own corrective RAG agent and become a champion of truth in the digital age.


What You’ll Need

  • Tools: Python, Llama 3, FAISS, Sentence Transformers, LangChain.

  • Skills: Basic Python programming, familiarity with AI models, and an interest in misinformation detection.


Step 1: Prepare Your Input Article

Start with a 500-word news article on climate change. You can write your own article or find one online (e.g., from a news outlet or blog). The article should include several claims about climate change, such as “Global temperatures have risen by 2°C since 2000” or “Deforestation accounts for 50% of global emissions.” Some of these claims may be inaccurate, which your agent will detect and correct.



Pro Tip: Include a mix of true and false claims in your article to test the agent’s fact-checking capabilities.

Step 2: Gather Your Dataset

To ensure your fact-checking agent has access to accurate information, collect 10 trusted articles on climate change from reputable sources, such as scientific journals, government reports (e.g., IPCC), or organizations like NASA and NOAA. This dataset will serve as the knowledge base for your RAG system, allowing your agent to verify claims with reliable data.


Step 3: Set Up Retrieval-Augmented Generation (RAG)

RAG is the backbone of this project, enabling your agent to retrieve accurate information for fact-checking. Here’s how to set it up:


  • Use Sentence Transformers to convert your dataset of articles into embeddings (numerical representations of the text).

  • Store these embeddings in FAISS, a library optimized for similarity search.

  • Implement a retrieval function that fetches relevant information for a given claim (e.g., “What is the actual rise in global temperatures since 2000?”).


With RAG in place, your agent will have a solid foundation for verifying the article’s claims.



Step 4: Design Your AI Agents

This project uses two AI agents to handle the fact-checking process. Here’s how to set them up:


  • Fact-Checker Agent: Uses the RAG system to retrieve relevant information from your dataset and verify each claim in the article. For example, it might find that global temperatures have risen by 1.1°C since 2000, not 2°C as claimed.

  • Corrector Agent: Analyzes the Fact-Checker’s findings, flags incorrect statements, and suggests corrections (e.g., “Correction: Global temperatures have risen by 1.1°C since 2000, according to IPCC reports.”).


You can implement these agents using LangChain, a framework that simplifies building AI-powered applications with multiple agents.


Step 5: Execute the Workflow

Here’s how your agents will work together to fact-check the article:

  1. The Fact-Checker Agent processes each claim in the 500-word article, using RAG to retrieve relevant information from your dataset and verify accuracy.

  2. The Corrector Agent generates a report that flags incorrect statements and provides corrections, citing the trusted sources used for verification.

  3. Combine the original article and the fact-checking report into a single text file for easy review.


For example, if the article claims “Deforestation accounts for 50% of global emissions,” but your dataset shows it’s closer to 15%, the report might say:


Inaccuracy: “Deforestation accounts for 50% of global emissions.”
Correction: Deforestation accounts for approximately 15% of global emissions, according to NOAA.

Step 6: Finalize Your Deliverables

The final output of this project will be:

  • A text file containing the original 500-word article on climate change.

  • A detailed fact-checking report within the same file, listing all inaccuracies and their corrections, with references to trusted sources.



💡 Real-World Use Cases

  • AI-powered journalism tools

  • Compliance bots for finance, health, or legal publishing

  • Content moderation for platforms fighting fake news

  • Browser plugins for instant fact-checking while reading



🧑‍💻 Perfect for...

  • Final-year engineering or data science students

  • AI startups in the misinformation detection space

  • Research teams working on ethical NLP systems

  • Media companies investing in trust tech



🚀 Get Help from Codersarts AI

At Codersarts, we build custom NLP pipelines, AI agents, and LLM-integrated tools to power the next generation of intelligent systems. If you want expert support building your Corrective RAG Agent, we’re ready to help.


💬 Book a FREE consultation with our AI team
🧠 Get guidance on agent orchestration, vector databases, and prompt tuning

Comments

bottom of page