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
How to Get Enterprise RAG Right
5 Principles for Building Enterprise-Ready Retrieval-Augmented Generation
Read this guide to learn:
- Why RAG represents transformative potential for enterprises
- What challenges enterprises can expect when implementing RAG
- Five considerations for building and scaling an enterprise-ready RAG architecture
- How to get started with enterprise RAG
A Sneak Peak into the Report
RAG has emerged as a promising solution to mitigate GenAI risks
Whether it's answering customer questions or streamlining complex workflows, GenAI gives businesses the tools to innovate faster and run more effectively than ever before.
But GenAI comes with well-known risks such as:
- Reliability of Generated Content (Hallucinations): Generating content that seems accurate but is factually incorrect.
- Bias in Results: Reflecting and perpetuating biases present in training data.
- Privacy and Data Concerns: Unintentionally including sensitive information from training data to unauthorized users.
- Information Frozen in Time: Knowledge limited to the model's last training date.
- Lack of Domain-Specific Knowledge and Nomenclature: Lacking specialized knowledge and industry-specific terminology.
- Lack of Control Over Responses: Providing inconsistent responses.
By combining the strengths of retrieval systems with generative models, retrieval-augmented generation offers a promising solution to mitigate these well-known risks.
But implementing RAG in enterprises is easier said than done
Implementing a production-ready RAG application at enterprise scale involves overcoming a series of challenges, such as:
- Inability to access and understand large volumes of unstructured content
- Key contextual elements become lost when ingesting complex, multi-modal content
- Lack of control and guardrails lead to inconsistent responses and hallucinations
- Data governance and compliance is compromised
Concerns about where data is stored and who has control over it - Potential for unauthorized access, sharing, or theft of intellectual property
- Inability to ingest vast content libraries up to millions of pages
- Multimodal content from diverse sources is too complex
- Latency issues arise due to the volume and variety of content processed “Picks and shovels” approach is time-consuming to scope, design, deploy, and evaluate
- Work is often duplicated or restarted to support multiple use cases simultaneously
- Generative AI models are often very expensive to operate and scale
5 principles for building enterprise-ready RAG
Not every RAG solution is fit to meet the needs of enterprises. Implementing RAG in a large organization requires careful consideration of five principles to ensure success:
- Understanding the content wherever it is, as it is
- Understanding the question
- Matching the best answer(s)
- Delivering a delightful use answer experience
- Adhering to security, governance, and operational requirements
Learn what it takes to get RAG right
Implementing RAG in an enterprise setting is a challenging endeavor, fraught with many pitfalls and hurdles. Before embarking on your RAG journey, it’s crucial to understand what’s needed to navigate this complex terrain.
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.
