What Is Retrieval-Augmented Generation (RAG)?

Large language models (LLMs) — the force behind generative AI — can do everything from answering complicated questions to creating original content. However, businesses face a key challenge with LLMs: data limitations. That’s where retrieval-augmented generation (RAG) comes into play.

RAG allows companies to connect their data with LLMs, enabling artificial intelligence opportunities for businesses that are more trustworthy, pertinent, and timely. For example, once the connection is made to internal data with RAG, autonomous AI agents can deliver customer service responses that take into account past questions or generate marketing briefs based on current brand guidelines.

Let's take a look at exactly what RAG is, what benefits it can deliver for your business, how it works, and how to get started.

What we'll cover:

• What is retrieval-augmented generation (RAG)?
• What are the benefits of retrieval-augmented generation?
• How does retrieval-augmented generation work?
• What are the different types of RAG?
• What is a RAG architecture LLM agent?
• How retrieval-augmented generation can help your teams
• Examples of retrieval-augmented generation by industry
• How to get started retrieval-augmented generation

Retrieval-augmented generation is a technique that delivers better generative AI results by enabling companies to automatically provide the most current and relevant proprietary data to an existing LLM.

An artificial intelligence (AI) model is only as good as what it’s taught. For it to thrive, it needs the proper context and reams of factual data — not generic information. An off-the-shelf LLM is not always up to date, nor will it have trustworthy access to your data or understand your customer relationships. That’s where RAG models can help.

Through RAG, companies can have AI models draw from the most up-to-date internal information. That’s not just structured data, like a spreadsheet or a relational database. This means retrieving all available data, including unstructured data: emails, PDFs, chat logs, social media posts, and other types of information that could lead to a better AI output.

Retrieval-augmented generation is a cost-effective approach that boosts your AI strategy by delivering higher-quality employee and customer experiences. Some of the key business benefits of using a RAG model include:

• Timeliness and relevance: By providing access to the most current internal company data, RAG enables LLMs to generate more opportune responses. Fresh information and learning from past interactions often means an increase in quality.
• Greater trust: A key challenge with off-the-shelf LLMs can be hallucinations — providing responses that are inaccurate or incorrect. A RAG LLM, which gives a company’s proprietary information to an existing model, reduces the likelihood of this by grounding generation in vetted and up-to-date company data.
• More control: Off-the-shelf LLMs are typically trained on an opaque and far-ranging set of data sources. With a RAG LLM, you can gain more control by augmenting general data with information from sources you specify.
• Enhanced search: Today's businesses grapple with a flood of insights, interactions, and information from many different sources. RAG improves search functions by applying the advantages of AI to company data.

RAG uses semantic search to retrieve relevant snippets of information from any data source, including a company's internal customer data platform, that contains information that is outside of what the LLM was trained on. These snippets are then used to deliver generative AI responses that incorporate the business's knowledge base — an outcome sometimes referred to as "grounded AI generation." Grounded AI generation can help you get better AI answers .

The core elements of RAG are:

• Pre-processing and indexing: RAG AI begins with taking a company's trove of valuable unstructured data — customer notes, emails, PDFs, chat logs, etc. — and connecting them (“pre-processing and indexing”).
• Retrievers and retrieval: Next, RAG uses powerful semantic search tools (“retrievers”) to sift through the business's internal data and find (“retrieve”) what's needed for a specific query.
• Grounded AI generation and augmentation: Finally, the RAG LLM takes the retrieved snippets of information and incorporates them into its response generation to deliver the most relevant answer (“grounded AI generation and augmentation”).

Retrieval-augmented generation encompasses several approaches toward organizing, connecting, and retrieving data. These are often related and include:

• Vector-based RAG: RAG can work in conjunction with a specialized type of database called a vector database to store information in a numeric format that makes sense to AI.
• Knowledge graphs: With knowledge graphs, data is organized around nodes and relationships, enabling RAG AI to make connections similar to how humans make them.
• Ensemble RAG: More than one retriever is run simultaneously, and the results are then combined. This allows for one model to compensate for another's weaknesses and enables responses to be cross-checked.

A RAG architecture LLM agent starts with the retrieval-augmented generation technique and a large language model. The "agent" part refers to adding an autonomous agent, also known as an AI agent, to the mix.

AI agents are an advanced form of AI that can independently execute tasks and learn as they go. They are created using an agent builder and rely on machine learning and natural language processing (NLP). When an agent is built on top of LLMs and a RAG architecture, it can engage in nuanced and context-aware interactions specific to the business while continually adapting and improving.

RAG can unlock efficiency and drive greater success across your entire organization by unifying LLMs, a cloud-based data engine, a customer relationship management (CRM) system, and conversational AI. With this combination, you can create a fleet of powerful AI agents tailored to each department's needs. These go beyond simple chatbots, acting as highly capable digital assistants integrated into workflows, constantly processing fresh information and continually learning.

What does this look like in action? Some examples include:

• Service: A service AI agent enables you to provide customer service powered by your CRM platform that's faster, more efficient, and more helpful. Using the data linked via the RAG technique, an AI agent can engage in personalized interactions, deliver proactive support based on anticipated needs, and manage service across multiple channels.
• Sales: With a sales AI agent integrated into your CRM, you can accelerate pipeline growth and revenue. An AI agent can tackle everything from autonomously nurturing inbound leads to providing coaching for reps, boosting the productivity and capabilities of your sales team.
• Marketing: A platform that has a marketing AI agent can deepen customer engagement and boost team productivity. With an AI agent, marketers can generate personalized content and offers across touchpoints, deploy smarter campaigns that reach customers throughout the entire customer lifecycle, and optimize autonomously based on the latest campaign KPIs.
• Commerce: A commerce AI agent that's part of a unified platform can boost sales by using unified customer data to provide personalized shopping experiences, deliver relevant content, and streamline ordering. It can also help on the back end with tasks such as optimizing inventory and generating product/service descriptions.

The benefits of RAG also span industries and business sizes. From small businesses to startups and beyond, the combination of AI and internal customer data can enhance experiences and support both employees and customers.

For example, an AI agent for a financial services company could draw on customer information to surface relevant insights for representatives and tailor recommendations based on an individual's specific financial goals. Similarly, an AI agent in the healthcare field could answer a patient's questions and help schedule the best provider for their needs.

In manufacturing, an autonomous agent could be used to monitor equipment and optimize production processes. And in the auto industry, a RAG AI agent could help across the board, doing everything from creating promotions based on real-time inventory levels to proactively surfacing vehicle maintenance issues.

With the right technology, getting started with RAG does not have to be costly or cumbersome.

Your foundation should be a unified platform with a powerful data engine to take on a wide variety of sources and file types. It also needs to connect all the data so it's optimal for retrievers, such as with a vector database. Your platform should have a sophisticated agent builder, like Agentforce does, that gives you the ability to create and customize autonomous AI agents to support your employees and customers.

A key aspect to remember as you dive in is that the success of RAG is intertwined with the quality of your chosen LLM. To combine the right RAG LLM, prioritize using a high-quality model with reliable, precise, and faithful contextual generation abilities. Keep in mind that the human element remains crucial. The better the query, the better the response, so help your people understand how to write a good prompt.

Ultimately, retrieval-augmented generation is all about the return on your AI investment. By pairing your data with generative AI, you take AI agents to the next level, making their responses and executions more personalized, relevant, and timely.

For example, Agentforce — the agentic layer of the Salesforce platform — uses this technology to help businesses get more done quickly. The Atlas Reasoning Engine is the brain behind Agentforce and uses RAG to help analyze information and determine how to best complete requests or tasks.

RAG architecture LLM agents can unlock benefits across your business, enabling you to build stronger customer relationships, optimize operations, improve marketing and sales performance, and grow efficiently.