A technique called retrieval augmented generation (RAG) continues to gain momentum as one of the most valuable approaches for making generative AI (GenAI) applications more reliable and useful. But what exactly is RAG, and why should you be excited about its potential? In this updated look at RAG, we'll break down the basics in plain English and explore how recent innovations are making this technology even more powerful.
What Is RAG?
RAG is an AI framework that enhances large language models by combining them with your organization's data and knowledge. It allows you to enhance the GenAI application by uploading text, PDFs, presentation slides, and images so that its answers are more accurate and grounded in your organization's data. A key benefit of RAG is that it gives the GenAI application access to domain-specific knowledge without retraining the entire model.
At its core, RAG combines three essential elements: retrieval, augmentation, and generation. Imagine your brain as an incredibly smart librarian. When faced with a question, this librarian doesn't just generate an answer from memory. Instead, it searches through a vast library of information, retrieves the most relevant details to supplement its existing knowledge, and then crafts a new, tailored response. Similarly, the RAG process involves three steps:
- Retrieval: Extract relevant information from a knowledge repository (e.g., text, PDFs, images, presentation slides) in response to a specific query.
- Augmentation: Enhance the input prompt by adding specific information gathered from these retrieved sources, enriching the AI model's understanding with additional data and context.
- Generation: Use the AI model's capabilities on this augmented input to produce a more informed and contextually appropriate response.
In simpler terms, when you ask a question to an AI system using RAG, it automatically searches through your organization's information, like documents, policies, or databases, selects the most relevant content, and adds this information to your question before sending it to the AI model. The model then combines its built-in knowledge with this retrieved information to create a response that's both informed by general knowledge and your specific organizational content.
Why RAG Is Incredible (and Getting Better)
Recent advances in RAG have made it even more powerful. It enhances GenAI applications by providing highly accurate, relevant, and useful information to organizations that use them.
1. Enhanced Problem Solving With Better Context
Knowledge workers like customer service representatives, technical support agents, and legal analysts must often reference policy manuals, case law, and other materials to answer questions accurately. RAG enables these professionals to get AI-powered answers that are grounded in their organization's specific information.
New advancements in RAG techniques have made this process even more powerful. New "hybrid search" capabilities combine semantic understanding, or grasping the meaning behind words, with precise keyword matching. This is particularly valuable when dealing with industry-specific terminology or technical jargon. For instance, in healthcare, financial services, or engineering, where exact terminology matters, these improved search methods ensure both conceptual relevance and terminological precision.
2. Stronger Risk Mitigation and Quality Control
One of the biggest challenges with AI systems is preventing "hallucinations,” which refers to GenAI outputs that are plausible-sounding but contain fabricated information. RAG helps address this by ensuring responses are grounded in the source material you control.
In the Dataiku platform, we've strengthened this protection with "quality guardrails" that act like an automatic fact-checker for AI responses. These systems evaluate each response for:
- Faithfulness: How accurately does the response reflect the source documents?
- Relevancy: How well does the response address the actual question?
Organizations can set specific quality thresholds and define what happens when responses don't meet these standards – whether providing a custom fallback response or an explicit notification that reliable information isn't available. This is particularly crucial in regulated industries where accuracy isn't just preferred but legally required.
3. Comprehensive Information Processing
Until recently, one of RAG's limitations was its focus on text-only information. However, organizational knowledge doesn't live in text alone — critical insights often appear in tables, diagrams, and images embedded within documents.
The latest RAG systems can now process multiple types of content simultaneously, including text, tables, and images within documents. This multimodal approach ensures that valuable information isn't missed simply because it's in a different format. It's like having a team of specialists analyze your documents, with one person focusing on text, another on tables, and a third person on images, and combine their insights into a comprehensive understanding.
4. Efficiency and Consistency at Scale
RAG continues to excel at efficiently sifting through massive amounts of information, delivering only what's relevant to the question at hand. This improves the quality of GenAI outputs, helping to prevent information overload and allowing decision-makers to focus on what matters.
With 24/7 availability, RAG systems ensure consistent access to organizational knowledge across different time zones and departments. This is particularly valuable for global enterprises, as it transforms organizational knowledge, or information known only to specific individuals, into accessible corporate knowledge that anyone can access at any time.
The Evolution of RAG
As AI continues to advance, RAG technology is becoming increasingly sophisticated and accessible. Recent innovations, including hybrid search for precise retrieval, multimodal document processing, and quality guardrails, represent significant steps forward in making RAG more powerful, reliable, and comprehensive.
These advancements aren't just technical improvements; they're transforming how organizations can interact with their information. From customer service to legal compliance and from product development to knowledge management, RAG enables more intuitive, context-aware AI that enhances decision-making and information access across the enterprise.
Today’s workers face cognitive overload, with many concepts and details to remember and manage, hour to hour. When they can quickly access and leverage organizational knowledge, it’s a competitive advantage for your organization. The RAG technique, with its continued evolution and improvement, is helping organizations turn their vast information resources into actionable insights, which is making AI not just smarter but more trustworthy and valuable for real-world applications.
By implementing RAG systems, your organization can ensure that your AI applications provide accurate, relevant, and comprehensive responses. This will allow you to create better, more consistent experiences that build trust and deliver value to employees and customers.