Do LLMs Hallucinate? Reading Meta's RAG Paper So You Don't Have To

Do LLMs hallucinate?

To answer this, I read Meta’s RAG paper so you don’t have to. Here’s my TLDR.

Recently I was reading the foundational paper by Patrick Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” (2020) from Meta AI. It is quite interesting how this paper changed the direction of innovation in relation to NLP.

The Problem

📌 The problem - LLMs sound confident even when they’re wrong.

So it would be right to say they don’t fetch facts, rather they guess.

The Solution

📌 The solution (from the paper): RAG which combines -

👉 A retriever that searches real documents

👉 A generator (LLM) that uses those documents to generate accurate responses

Key Learnings

💡 The 3 most important things I learned:

1️⃣ RAG beats fine-tuning for tasks like open-domain QA

2️⃣ It uses vector search, not keywords—so it’s smarter

3️⃣ You can plug in your own docs, PDFs, or company data

Why It Matters

📌 Why it matters:

Imagine ChatGPT that reads your company’s documentation and responds with citations. That’s what RAG enables—and that is the reason it proved to be a game-changer.

💬 Are you working with RAG? Let’s connect and share insights 👇