Everything You Need to Know About Building a RAG Architecture
Learn why RAG architecture matters, how it works, and how to build an effective RAG solution for your organization
AI for Sales and Service
4 key considerations for using generative AI in organizations with technically rich content
Read this guide to learn:
- What knowledge friction is and why it’s a productivity killer for your employees, customers, and partners
- How leading organizations use GenAI solutions to plug critical knowledge gaps, even for the most complex technical content.
- Key considerations to keep in mind when implementing GenAI solutions
A Sneak Peak into the Report
Sell, service, and support technical products and equipment? You might have a knowledge friction problem.
Field technicians, customer support agents, channel partners, and sales managers all need quick access to information to do their jobs effectively, ranging from maintenance tips to warranty policies. Yet, too often, hunting for this information is a struggle — in fact, 70% of knowledge workers spend an hour or more searching for a single answer.
Where generative AI can help
Generative AI solutions can uncover answers hidden inside technically rich content and deliver them where and when they're needed. But not just any GenAI tool will do. Businesses selling, servicing, and supporting engineered products often have a ton of complex, multimodal content that's continuously being refreshed and is scattered across a myriad of sources. In other words, your content isn't just hard to find, but also challenging for machines to understand. A GenAI solution capable of reading your content — including schematics and tables — just like a human can democratize access to trusted knowledge, streamline workflows, and improve decision-making.
Retrieval-augmented generation is the right way to implement GenAI to solve knowledge friction.
You can't afford to deliver incorrect or outdated information to your employees, partners, and customers. More and more businesses with revenue on the line are turning to retrieval-augmented generation (RAG) AI technology. With RAG, your trusted content (and only your trusted content) serves as the basis of generative outputs. That means the answers people receive are trustworthy, fact-based, and traceable to their sources.
4 key considerations for implementing RAG with technically rich content
How can you make sure you're implementing RAG right? Businesses with technically engineered products need to consider four critical factors when deploying RAG to solve knowledge friction:
- Accuracy: Can the RAG solution really understand your content?
- Security: Is your content safe from prying eyes and cyberthreats?
- Scalability: Can the RAG application manage high volumes of complex data and grow along with your needs?
- Speed: How quickly can you get up and running?
Learn what it takes to get RAG right
Implementing RAG in an enterprise setting, especially when your content is technical in nature, is easier said than done. Find out how to quickly unlock the value of GenAI for your employees, partners, and customers while ensuring high accuracy and security.
Enterprise RAG Starts with Pryon.
Pryon RAG Suite provides best-in-class ingestion, retrieval, and generative capabilities for building and scaling an enterprise RAG architecture.
With Pryon RAG Suite, enterprises and government agencies can easily build safe and secure generative AI solutions that source answers from organizations’ trusted content.
