🚫 Why Most RAG Systems Hallucinate — and How We’re Building One That’s More Reli

🚫 Why Most RAG Systems Hallucinate — and How We’re Building One That’s More Reliable ✅

In the age of generative AI, Retrieval-Augmented Generation (RAG) is a game changer — but it’s not without flaws. If you’ve ever asked an AI a question and received a confident, yet completely wrong answer, you’ve witnessed hallucination in action.

At LINAGORA, we’ve been building OpenRAG — a fully open source RAG framework — from the ground up to make outputs more accurate and trustworthy. In this post, we’ll explore why most RAGs hallucinate, and how we’re working to fix it with OpenRAG.

🧠 What is a RAG hallucination ?

A hallucination is when a system generates something that sounds correct but is factually wrong — even when it retrieved documents properly. In enterprise and public sector use cases, this can’t be ignored.

⚠️ Why do RAGs hallucinate?

We’ve found three common causes:

• Ambiguous user queries • Poor quality in retrieved documents • Over-reliance on generation

Let’s break down each one and how OpenRAG addresses them.

✅ Ambiguous queries

When prompts are vague or unclear, retrieval struggles. Instead of guessing, we built a pipeline that includes:

• Multi-query retrieval: reformulates questions into diverse interpretations • Conversational context: uses chat history to resolve ambiguity

This leads to more relevant context and more accurate answers.

✅ Poor relevance in retrieved documents

You can’t generate good answers from bad input. So we focused on improving retrieval quality — not just generation.

• Custom pipelines for PDFs, DOCX, PPTX, emails, audio, images • OCR and image captioning to make visuals searchable • Context injection: each chunk includes source and summaries of earlier content • Smart chunking strategies to preserve meaning

These lead to cleaner, more relevant inputs that support stronger generation.

✅ Over-reliance on generation

Some RAGs let the LLM “fill in the blanks” with no solid evidence. We did the opposite:

• Prompts that require grounding in retrieved content • Full citations at the bottom of each response for traceability

🧪 Evaluation that works

We didn’t stop at building — we measure. OpenRAG includes an evaluation pipeline that:

• Builds test sets from your own documents • Uses LLM-as-a-judge to compare configs • Helps you pick the best setup for your needs

✅ Built for the real world

Whether you’re working with legal docs, tenders, emails, or internal wikis — OpenRAG helps you get not just answers, but answers you can trust.

Want to see it in action?

👉 Join our webinar on July 10 at 5:30 PM (CET) https://lnkd.in/eGJVdETQ

Let’s build RAG systems that don’t hallucinate.

🙏 Huge thanks to the LINAGORA team building truly open source AI: Ahmath GADJI, Thanh An Nguyen, Camille Bizeul, Houssem Tagourti, Lydia Passet, Matteo Van Ypersele, Paul Tran-Van, Ulysse Bouchet, Benjamin Bellamy, Michel-Marie MAUDET, Alexandre Zapolsky — and everyone involved!