AI is rapidly integrating into the enterprise landscape—so much so that it’s hard to find a team not using AI in some capacity today.

But even advanced AI isn’t perfect. One persistent issue is hallucination: when models confidently generate false or misleading information. Since the launch of ChatGPT, this problem has remained top of mind. Perhaps it’s because AI mimics human intelligence so closely—even its tendency to make mistakes.

In this article, we’ll introduce two powerful technologies that help reduce hallucination in AI: RAG (Retrieval-Augmented Generation) and LoRA (Low-Rank Adaptation).

What Are RAG and LoRA?

Both RAG and LoRA aim to help language models provide more accurate, context-specific responses. While ChatGPT is trained on vast amounts of general knowledge, it can’t tailor answers to your company’s unique context or niche regulatory environments—like local traffic laws or proprietary internal data.

This is where RAG and LoRA come in. They enhance LLM performance in different ways and excel in distinct use cases. Let’s break down how they work.

🧠 What is RAG?

RAG stands for Retrieval-Augmented Generation. It allows an LLM to search external knowledge sources (such as documents, PDFs, or databases) in real time and generate responses based on that retrieved content.

A well-known example is Google’s NotebookLM, which analyzes uploaded documents and crafts answers grounded in that content.

Key Features of RAG

• Augments LLMs with real-time external data

• No retraining required (no fine-tuning)

• Enables citation-based, verifiable answers

🔧 What is LoRA?

LoRA (Low-Rank Adaptation) fine-tunes only a small portion of an LLM’s parameters using a low-rank matrix, allowing the model to adopt domain-specific language styles or knowledge without the high cost of full model retraining.

Want to create an AI that understands legal documents? LoRA lets you embed legal tone and structure directly into the model.

Key Features of LoRA

• Lightweight fine-tuning of existing models

• Reduces GPU usage by 90% compared to full fine-tuning

• Improves domain-specific accuracy without sacrificing core performance

RAG vs. LoRA: Key Differences

Usage Differences:

• RAG tools refer back to documents at query time.

• LoRA-enhanced models embed knowledge and don’t need external references during use.

Final Thoughts: Choose the Right Tool for Your Needs

RAG is your real-time knowledge engine. LoRA is your customized cognitive circuit.

For truly effective enterprise AI:

• Use RAG to ensure trustworthiness and up-to-date responses.

• Use LoRA to embed domain-specific tone, format, and insights.

Wissly combines both—helping companies extract insights from stored documents with tailored, accurate answers.

Ready to deploy smarter AI in your workflows? Explore Wissly’s enterprise AI solutions