Since the emergence of generative AI, large language models (LLMs) have developed into effective tools for processing and generating information. New generative LLMs can now handle tasks like sentiment analysis, language translation, and text generation thanks to advancements in deep learning. This increases the value of LLMs for organizations looking to improve efficiency and productivity while maintaining a secure environment.

LLMs are highly advanced in their capabilities, but they face two key challenges that need to be addressed before businesses can deliver outsized value. First, as algorithms trained on datasets fixed in time, models can be prone to hallucinations—that is, producing fabricated information or language that may sound right but isn’t accurate. They can also lack up-to-date information and domain-specific knowledge, which can impact their ability to mitigate these hallucinations and deliver relevant information.

The tech industry’s solution to this is a process known as retrieval-augmented generation (RAG). It enhances model performance by integrating company-specific knowledge and real-time data from both corporate and external systems. This integration helps the generative model to refine—or, in essence, augment—outputs that are more accurate and relevant. Some RAG implementations can also provide in-line citations to help the end user follow the information trail, helping users to better trust and audit the output.

How might RAG impact enterprise generative AI? Let’s dive into the technology and discover why RAG is here to stay.

RAG meaning: What is retrieval-augmented generation?

In simple terms, retrieval-augmented generation, better known by the acronym RAG, is a method for enhancing the quality of an answer generated by an LLM through connecting the model to an external data source. In practice, this means the output of an LLM can be complemented by more sources than just those it was trained on. RAG connections give a model additional context that can inform its replies, mitigating the risk of hallucinations and improving performance.

How does retrieval-augmented generation work?

A generative AI model functions essentially as a “black box”—meaning the magic happening within is not easily interpretable. The model excels at recognizing statistical relationships between words, but can be inaccurate in understanding their meaning in context. That’s why it might hallucinate and generate incorrect answers.

In contrast, RAG brings real-time data and domain expertise into the model through the use of tools and API connections. It’s a process of optimizing the output of a generative model, teaching it to reference an authoritative knowledge base outside of its training data before generating a response.

For this reason, RAG is now a preferred method for addressing the drawbacks and challenges of GenAI. IBM Research’s director of language technologies, Luis Lastras, has a great analogy: “It’s the difference between an open-book and a closed-book exam. In a RAG system, you are asking the model to respond to a question by browsing through the content in a book, as opposed to trying to remember the facts from memory.”

Among the researchers credited with laying the groundwork of RAG is Cohere’s Patrick Lewis, co-author of the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. By virtue of his and others' contributions to the advancement of systems that combine retrieval-based information with generative AI, businesses and organizations can now rely on RAG for more precise and contextually-appropriate outcomes by using data that is current and pertinent.

RAG use cases

RAG has loads of potential for enterprise, especially in optimizing corporate search. It can ease information retrieval from manuals, videos, logs, and other structured and unstructured datasets by turning them into an LLM knowledge base. This is a great step forward for organizations looking for more functional workflows, putting company-wide knowledge at their fingertips. RAG makes achievable what knowledge management systems have been promising for decades.

Through RAG, you could support employees by enabling quick answers to common HR and operational questions. You could build a fully conversational AI wellness coach that considers demographics and history. But arguably one of the most enticing use cases for RAG is that of an AI knowledge assistant or research agent. These types of applications can cite sources in their responses, enabling users to follow an information trail and verify the answer generated by the model.

Adding features like multi-step tools—that is, tools that allow the model to perform a series of actions—to RAG solutions can open the door to even more advanced tasks, such as data entry automation or code updates. This is referred to as agentic RAG.

Retrieval-augmented generation examples

Seeing how retrieval-augmented generation is already used in the real world can help show how it’s impacting different industries.

Financial services

Finance companies can leverage RAG to change how they access and analyze their data. Investment advisors, for example, can work with a RAG-based solution that integrates data from sources such as financial news outlets or stock market trackers, and use it to steer recommendations to clients. A recent poll suggests that nearly a quarter of independent financial advisors are considering implementing AI in their services to clients.

With regulatory compliance an ever-present challenge in finance, the use of secure generative AI, enhanced by RAG and linked to a corporate knowledge base, can offer a powerful solution to achieve broad workflow automation with more consistent accuracy.

Healthcare and life sciences

With the risk of inaccurate outputs too high for some healthcare organizations to consider implementing AI tools, RAG’s ability to attribute sources and access new data could prove the turning point for AI in healthcare.

Imagine a doctor having access to a RAG-powered assistant linked to patient records, up-to-date guidelines, and effectively unlimited medical journals. This could cut down on time spent poring through research or trying to pick out viable diagnoses, freeing-up the clinician to focus on better patient care.

Public services

Public services that need to provide accurate information, especially in times of crisis or need, could benefit from using RAG-driven enterprise AI to keep the public up-to-date.

Public health bodies, for instance, could offer advice that’s in line with official government stances and relevant recent developments. They may also use RAG to accelerate the operations of crucial emergency services, reducing the risks posed by issues such as weather crises or infrastructural disasters.

Energy and utilities

Efficiency is a key driver in the energy sector, an industry that produces vast amounts of data every day. RAG systems are well-equipped to, for example, suggest supply optimizations based on energy grid metrics, historical precedent, energy generation from different sources, and other information. In a market that’s increasingly regulated for sustainability, these optimizations could prove a differentiator.

Manufacturing

Using RAG-driven AI to draw upon technical specifications and real-time equipment data, manufacturing companies can closely monitor the health of their machine hardware. This can allow them to optimize their maintenance scheduling and extract maximum performance from their production lines. They can also use RAG to anticipate and detect disruptions to their supply chains, giving them opportunities to react accordingly.

Education and research

Cross-referencing is often vital for researchers and decision-makers within organizations, and systems bolstered with RAG can take away much of the burden of verifying and checking sources. Businesses may also benefit from RAG by linking it to company-specific data such as market trends and past performance.

Benefits of retrieval-augmented generation

One of RAG's key benefits is its ability to deliver deep, contextual understanding when combined with semantic search. Together it helps create a more reliable and well-organized database of information. Additionally, with source attribution, RAG systems build trust and credibility by showing where information comes from.

But those aren’t the only benefits. No matter the source—whether internal data stores or external web sources—retrieval tools enhance generative models by providing domain expertise, extending their capabilities without the need for frequent and costly retraining. That has further advantages, such as:

Improved answer quality: Allowing the model to access larger data sets and more information simultaneously gives it a better understanding and supports more accurate results.
Mitigates known challenges: Some of the limitations of generative AI—such as knowledge cut-off dates, terminology confusion, and false information or hallucinations—can be partly mitigated by RAG.
Enhanced reliability: RAG ensures the model has access to the most current information, and hooks it up to authoritative, predetermined knowledge sources to enhance the reliability of information retrieved.
Better flexibility: With RAG, a model is not restricted to its internal knowledge when generating its answer. It can be trained to become a research agent, digging deeper into the available documentation across multiple sources, and learning to course-correct when it hits a blocker.
More developer control: Importantly, RAG also allows developers to test and improve their AI-driven applications more efficiently, and to control and change the model’s data sources to adapt to changing requirements. This means that businesses can adapt quickly to changing needs and provide AI tools that remain relevant, without needing to wait for lengthy development cycles to update the models themselves.
Connectedness: When RAG is combined with multi-step tools, developers can build powerful AI agents that can access information from any connected database or service.

Challenges of RAG

Like any evolving technology, RAG is not without its challenges. While retrieval-augmented generation enables tool use and provides additional data sources for the LLM to draw from, that can also mean there are additional development resource costs. Each information retrieval component must be connected to each new source.

When choosing an LLM for your enterprise, it’s of course important to verify whether the models are trained and optimized for RAG solutions. This decision also impacts your budget. Ensuring that RAG resources remain up-to-date and current can be cost-intensive, but it’s an investment that may prove fruitful once your systems begin producing the expected results. If you want to skip the overhead, however, plenty of providers have done the heavy lifting already and offer their services and products. The costly parts of RAG (and, really, all LLMs) are computational power, skilled professionals, and storage. Evaluating your own resources will help you make an informed decision.

AI security is, of course, paramount. As always when dealing with most innovative technologies, securing information and resources should always be a priority. In the case of RAG, security-specific risks include exposure of sensitive data and increased vulnerability. As RAG systems pull data from external sources in order to integrate live data, they can be more prone to outsider attack and issues like malicious content injection (when harmful or false information is purposely added to the data sources) unless there is a proper security system in place. Strategies that can help safeguard your resources include: enforcing permission control to prevent unauthorized individuals to access content and data; continuous monitoring; and data tracking and filtering to avoid feeding the RAG models any malicious information.

Accuracy of results is not guaranteed, either. The additional context is helpful, but only if the provided data sources are up-to-date—and data sources can quickly become outdated, inaccurate, or contain bias. That’s one reason why citations with RAG systems can be particularly useful, allowing users to verify the information if required.

Even with these challenges addressed, you’ll need a robust yet accessible process for handling reports of inaccuracies or correcting information sources in those data sets and tools that are linked to the LLM. It’s also a good idea to implement robust training for end users in how to best work with RAG queries—you do require quality input to get quality results.

How to implement a RAG workflow effectively

If you’re interested in using RAG for your AI solution, you need to know what the RAG implementation process looks like. Here are some steps to follow.

1. Assess your organization's data infrastructure

What data do you currently store? Where does it come from, where do you keep it, and how do you format it? Beyond that, what data do you want to use?

Your RAG system will only function as required if you ensure it has access to data that’s accurate, well-structured, and sufficiently comprehensive.

Start by evaluating the current data infrastructure within your enterprise. Determine which data sources are most critical for your business operations and decide whether they will be internal (proprietary databases) or external (APIs, cloud repositories, and so on). Ensure these sources are reliable, secure, and up-to-date as they will form the foundation of your RAG implementation.

2. Build a suitable data pipeline

To access and store the data you need, you may have to gain access to external APIs or create internal databases.

Before making a choice, look into data aggregation services—services that allow systems to pull data from multiple external sources—that may have everything you need already conveniently packaged. But having the data ready to be accessed isn’t enough; you also need reliable automated processes for finding and retrieving necessary entries, and those processes must be capable of noticing inconsistencies.

Carry out extensive testing and use the results to get everything running smoothly before your final RAG roll-out. Without these initial checks, you risk needing to suspend operations to fix unseen issues, or even potentially letting inaccuracies slip through. This is particularly important to determine the best chunking approach to your data—that’s the process of breaking down large pieces of information into more manageable and context-rich chunks.

Conduct a lengthy sequence of test queries similar to those the live system will field, and have intended users assess the outputs to see how good they are. If everything looks solid right away, you can proceed. Otherwise, adjust your set-up until it’s ready for full implementation.

4. Deploy RAG and monitor performance

Even after testing, don’t make the mistake of rushing the rollout. Take it one process at a time to check nothing goes awry, and pay close attention to the metrics as they accrue. Do the response times stay low enough when all your processes are under load? Are users happy with the outputs?

Even when performance appears to be running smoothly, continue with semi-frequent assessments to maintain it. Checks should cover everything from stability and security to appropriateness for purpose—especially given this technology is constantly and quickly evolving. Ongoing learning and retraining of the model also fits into a healthy long-term monitoring plan, especially as new data sources are added or data quality shifts.

The impact of RAG on enterprise AI

As we have seen, expectations are great when it comes to RAG. This technology is revolutionizing enterprise applications and is set to continue to do so in the future. Its ability to greatly improve the dependability and overall effectiveness of systems—not just through reducing hallucinations and augmenting accuracy—is what makes RAG so compelling.

RAG grants generative AI systems real-time data retrieval options, mitigating their limitations by keeping their outputs rooted in accurate, relevant, and up-to-date information. Industries such as healthcare and finance can use RAG-empowered LLMs to help accelerate their processes without sacrificing output reliability. Doctors, for instance, may benefit from RAG's help in carrying out diagnostics much more quickly and effectively.

RAG is also resource-intensive and, consequently, a large investment for enterprises. Implementation requires robust data infrastructure, smooth data retrieval systems, extensive testing, and a commitment to ongoing refinement and updating for the system to function optimally.

Still, RAG has a bright future. The systems yet to come will be faster and equipped to use much greater data pools, giving them even broader applications. The long-term goal of RAG is to make generative AI systems more reliable and adaptable in real-world applications.

FAQs about RAG

How does a large language model work?

RAG is a supplementary technology to large language models, so it’s important to understand LLMs first.

To answer a user’s question, an LLM’s process turns written language into a dataset of tokens, which are then transformed into mathematical vectors via embeddings, and assigned to data plots to indicate the context of the word and how it’s used.

The contextualized data is then flowed through transformer blocks that use the attention mechanism—a way to tell words apart when they are used in different contexts, as described in the paper Attention is All You Need, co-authored by Cohere Co-Founder and CEO Aidan Gomez—to predict the next most likely word in a sentence.

Through this combination of word similarity scores and math, the LLM can generate an answer in natural language.

How does retrieval-augmented generation work?

Once a user inputs—or asks—a question and kicks off the LLM process of natural-language answering, the RAG-driven model adds additional steps to check with data sources before giving its answer.

First, the model generates search queries to find the relevant documents. It will consider what data sources to call on to get the appropriate information for this question. Then it moves to fetching that information from the external data sources using the generated search queries.

This phase of the process will vary depending on how the developer has built and connected the model. The model needs clear guidance on what sources it has available, and how it needs to work with those sources, to enable a smooth RAG methodology.

Now it’s ready to use the generative AI process of creating an answer. RAG-enabled models will use the acquired knowledge to produce an educated answer—and can then add in-line citations for the source of each fact in that answer. The user receives a well-researched and informative answer, and can then follow that information trail if needed.

What is RAG as a Service?

RAG as a Service (RaaS) is an example of one of many offerings of the as-a-service (aaS) model, and is part of AIaaS (AI as a Service). That means that RAG is provided as a fully-managed service by a provider. Much like Software as a Service (SaaS), RaaS offers businesses access to AI tools on a subscription basis, so that there is no need for them to implement and maintain their own infrastructure. This way, companies can integrate RAG into their systems and achieve the same benefits as using in-house RAG systems without overhead.

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RAG meaning: What is retrieval-augmented generation?

How does retrieval-augmented generation work?

RAG use cases

Retrieval-augmented generation examples

Financial services

Healthcare and life sciences

Public services

Energy and utilities

Manufacturing

Education and research

Benefits of retrieval-augmented generation

Challenges of RAG

How to implement a RAG workflow effectively

1. Assess your organization's data infrastructure

2. Build a suitable data pipeline

3. Conduct iterative testing and refinement

4. Deploy RAG and monitor performance

The impact of RAG on enterprise AI

FAQs about RAG

How does a large language model work?

How does retrieval-augmented generation work?

What is RAG as a Service?

Building enterprise AI agents

What is RAG? Understanding the latest evolution of GenAI