Retrieval-augmented generation (RAG) is a technique in natural language processing (NLP) that improves text generation by incorporating data from databases, digital asset libraries, or other knowledge sources. Imagine a legal team searching for precedents in a vast collection of case law documents. Through RAG, they could quickly get a summary of a retrieved document and identify key points that they can validate and use to support their arguments.

Thanks to artificial intelligence (AI), this process happens in seconds. RAG uses AI-driven large language models (LLMs) — trained on massive datasets of text and code — to generate accurate and contextual content based on sources like documents, spreadsheets, and presentations.

For your enterprise to fully take advantage of AI, you need to continuously feed its model with contextual and up-to-date information. That’s where RAG steps in, integrating real-time data to make sure the outputs are precise and relevant. Without RAG, your business would be stuck with pre-trained LLMs that can miss critical, updated context, leading to inaccurate results and the risk of AI hallucinations.

If you’re wondering whether RAG can exist without artificial intelligence, the short answer is no. The “generation” capability of RAG relies on AI systems, which means you need a generative model to produce answers to your questions or prompts. Let’s review how this process works.

How does retrieval-augmented generation work?

Suppose you’re an insurance analyst who needs to check if a certain type of damage is covered under an extensive property policy. By integrating AI into your document management system, you could use RAG to ask a targeted question that directly addresses the coverage status in a given policy.

For instance, if you submit a query like “Is fire damage covered under property insurance policy number 1234567890?,” RAG will follow these three steps:

1. Retrieval: A system, often based on semantic search, identifies relevant documents from your cloud data storage or any other knowledge source
2. Augmentation: The retrieved information is combined with an LLM’s pre-trained data to create a more informative prompt, integrating the LLM with additional context
3. Generation: The LLM generates an answer to the query, using the understanding of language and the information from your documents to create a more relevant response

As with any other type of AI-generated content, you must ask clear and specific questions to get precise answers with RAG. Other best practices to obtain fast and reliable responses involve including relevant details for context, clarifying your question when needed, and specifying the format for output — such as a step-by-step guide or a checklist.

Benefits of retrieval-augmented generation for businesses

The increased adoption of AI in enterprises is fueling the rise of the retrieval-augmented generation market. Acute Market Report forecasts this industry will grow at a CAGR of 31.5% between 2024 and 2032. Driving this growth is the need to scale initiatives like content generation and rapid information retrieval as data expands in volume and complexity.

By deploying retrieval-augmented generation tools, you benefit from:

• Contextual document retrieval: RAG enhances the quality of the information retrieved by considering the context of your queries. For example, in engineering, RAG tools can locate documents that are pertinent to a particular phase of a project or specific technical requirements. They prevent information overload and ensure teams access content that addresses their needs directly.
• Real-time information access: In departments like sales or customer support, RAG can help close a deal or prevent a client from leaving a bad review. Teams can consult if specific information is accurate and quickly address customer concerns based on your products and services — especially if you have comprehensive FAQs, user guides, and client onboarding documents.
• Faster query resolution: A Gartner survey revealed that 47% of digital workers find it challenging to locate the information or data they need to perform their jobs effectively. By using LLMs to generate text based on your content, you can ask questions across extensive contracts or policies and find answers without spending hours on research.
• Scalable content creation: Combining retrieval and generation allows teams to use existing information as a reference to automate the creation of product descriptions, personalized customer replies, or job advertisements following the same tone as previous models. It helps businesses with extensive content demands or teams that work with quick and consistent content updates, such as advertising agencies.

Top RAG use cases and examples

Before adopting AI in your enterprise to take advantage of RAG’s benefits, let’s take a look at the most common retrieval-augmented generation applications.

Discover how to use AI for business success.

Getting started with enterprise RAG for content management

Integrating RAG into your cloud content management system is the best way to ensure efficient document retrieval and processing, but remember that this process should be part of a broader AI strategy.

To implement RAG and AI effectively, follow these best practices:

1. Evaluate your enterprise AI readiness

While the pace of progress in artificial intelligence has accelerated in the last few years, only 31% of companies have a formal AI strategy in place, according to Asana’s State of AI Work. Before adopting RAG in your enterprise, develop a roadmap that aligns with your AI strategy to support this implementation.

How to get started:

• Audit your technical infrastructure: Make sure your data infrastructure, cloud storage capacity, and computing power can support large-scale AI operations, allowing you to handle more complex use cases and larger volumes of content
• Define objectives: Outline how RAG will enhance your content management workflows, such as improving search functionality, automating document summaries, or enhancing team members’ interaction with your digital asset library
• Identify key stakeholders: Involve IT, operations, compliance, and other relevant departments early in the process, fostering collaboration and alignment to support responsible AI initiatives

2. Select suitable RAG tools

RAG solutions vary based on factors such as retrieval systems, LLM architecture, and customization options. For example, some tools use semantic search algorithms to retrieve documents based on their similarity to the query, while others locate information through exact keyword matches.

Select an option that meets your business requirements, such as scalability, integration with existing software, and data privacy. Let’s say you need secure RAG models to protect sensitive customer information from retrieval. While you may find various options in the market, you should choose a system with advanced access controls and data classification to keep unauthorized users from accessing confidential files in your storage.

When evaluating potential tools, check case studies, client reviews, and expert opinions to get a clear picture of what each option offers. Test the tool’s security features, such as encrypted document sharing, granular access control, and password protection, to ensure they meet your company’s regulatory and compliance standards.

3. Prepare and organize content

According to a Box-sponsored IDC white paper, 90% of business data is unstructured. Implementing AI and RAG requires structuring content to ensure relevant answers. Improve the retrieval and generation processes with these best practices:

• Audit your current document repositories to eliminate outdated and redundant files, as RAG requires up-to-date, relevant content to deliver accurate results
• Add tags and categories to give context to your content and facilitate retrieval and processing by RAG tools
• Take advantage of enterprise metadata management to help AI extract information from your content more effectively
• Set robust data governance policies for information storage, retention, and access to ensure secure content management and compliance with legal standards

Integrate AI and RAG into your content lifecycle with Box

With Box, you streamline every stage of your content lifecycle, from drafting documents to searching for files in your storage system. The Intelligent Content Cloud enables you to put different use cases of retrieval-augmented generation into practice with the power of AI.

You don’t need to worry about spending extra time trying to find information or manually summarizing content. Box offers solutions to make RAG implementation faster, easier, and more scalable, including:

• Box AI: Integrate advanced AI models into your content management platform to locate documents in real time and summarize extensive contracts instantly
• AI principles: Have complete control over your use of AI with transparent and responsible guidelines that protect your content quality, confidentiality, and safety
• Box Hubs: Perform cross-document searches and receive reliable and comprehensive answers about existing topics so you can make more informed decisions
• Box Notes: Create emails, meeting agendas, and guides from scratch or based on existing models, ensuring consistency across your documents
• Enterprise-grade security: Protect your valuable information with AES 256-bit encryption, advanced authentication, device trust, granular access controls, and more

Contact us to explore all the tools and features to simplify AI adoption in your business.

While we maintain our steadfast commitment to offering products and services with best-in-class privacy, security, and compliance, the information provided in this blog post is not intended to constitute legal advice. We strongly encourage prospective and current customers to perform their own due diligence when assessing compliance with applicable laws.