Hallucinations, rising compute costs, limited resources, and the need for a constant flow of current and dynamic information, have all proven problematic for enterprise-grade LLM solutions.

That is, until the introduction of retrieval-augmented generation (RAG) systems.

As noted in the foundational paper by Cohere’s Patrick Lewis, “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” published in 2020, RAG provides the best of both worlds: timely, encyclopedic knowledge combined with large language models capable of generating language. In essence, RAG systems connect LLMs to real-world data.

This novel system combines the strengths of generative LLMs with more explicit, timely, and accurate information from defined sources, like in-house company knowledge systems, datastores, and even other SaaS applications like CRMs and ERPs. In doing so, RAG unlocks enterprise knowledge for daily operations and workflows.

These RAG-based LLM solutions are transforming how professionals work, offering tools in the shape of workplace assistants that can swiftly summarize external content, analyze customer data for tailored product recommendations, and enhance market research by extracting actionable insights from dense documents. These advanced applications not only save time, but they also drive smarter, data-driven decisions in fast-paced business environments.

Here’s a quick primer on why RAG is a groundbreaking innovation for Enterprise AI, and why leading technologists are choosing to build RAG systems to capture the most value for their business.

The elements of RAG

Let’s start with the basics. As the name suggests, RAG is made up of three essential steps:

Retrieval: First, the system searches for and pulls information from a data source. It can use a basic keyword search engine or more sophisticated semantic search with embeddings. Either way, the goal is to find the most accurate data to help the model in answering a user’s query.
Prompt Augmentation: Next, a user's query is combined with the retrieved documents to create a new prompt, providing context and guiding the model to use the documents to answer the query. Often, the model is provided with multiple documents or data and needs to reason which are relevant for answering or addressing the question.
Generation: Finally, the newly created prompt is introduced to the generative model, which then generates the final answer, leveraging the retrieved information. These answers can come with citations pointing to the original data source used providing transparency for verification.

Check out all three steps in a simple RAG execution illustrated below.

Creating value from RAG systems

Here are the top five reasons that companies are choosing to build LLM solutions with RAG pipelines.

1. Reduced hallucination and increased trust.

LLMs can sometimes generate incorrect or nonsensical outputs, known as "hallucinations,'' when they lack the required knowledge. By providing relevant external data, RAG mitigates hallucinations and increases user trust in the LLM's outputs, which are grounded in verifiable information sources. This is particularly true when testing for factual accuracy in domain-specific queries.

2. Improved accuracy and relevance of responses.

RAG allows LLMs to access and incorporate up-to-date, domain-specific information from external data sources like databases, documents, and knowledge bases. This enhances the accuracy and relevance of LLM outputs by grounding them in factual, contextual data rather than just the model's training data which can be outdated or lack specialized knowledge. Additionally, because RAG is a workflow involving multiple models and steps, you can tune each of the pieces to boost performance. For example, optimizing the retrieval step in RAG can lead to even greater accuracy, in some cases improving results by as much as 50%.

3. Ability to customize and personalize end solutions.

RAG allows businesses to customize LLM solutions by incorporating their proprietary data from internal knowledge bases, customer records, and other private information sources, including their CRMs and ERP solutions. This enables highly personalized and context-aware responses tailored to the specific business domain, products, services, and customer needs. For example, IBM is currently using RAG to ground its internal customer-care chatbots on content that can be verified and trusted. RAG can also be used to generate responses to fit a user’s specific needs, like Databrick’s documentation chatbots.

4. Scalability and adaptability.

Instead of retraining or fine-tuning an entire LLM, RAG allows businesses to update and expand the external knowledge sources as needed. This makes it easier and more cost-effective to keep LLM solutions up-to-date with the latest information and adapt to changing business requirements. By decoupling the retrieval and generation components, RAG allows for efficient scaling and serving of each component independently based on demand. It also lets you tailor results to specific users such as implement role or user level access to certain information, all without changing the base models.

For enterprises looking for cross-functional solutions that take advantage of existing datastores, an LLM trained with RAG capabilities, like Cohere Command R / R+, can be applied to multiple domains by switching the retrieval data sources, thus avoiding repetitive fine-tuning costs.

5. Auditability and transparency.

Some LLMs can provide citations when using a RAG system. Ideally, you’ll want to use a model specifically trained to generate accurate citations, like Command R / R+. These citations refer back to the external data sources used to generate and augment a response with RAG. While this does not completely solve the problem of hallucinations, the additional transparency improves auditability, explainability, and trust in the LLM's outputs, especially in regulated industries or high-stakes decision-making scenarios. Without proper citations, LLM outputs could potentially violate intellectual property rights or spread misinformation. Providing clear attributions to sources reduces legal risks and liabilities associated with the misuse or misrepresentation of information.This is especially relevant in regulated industries where there are strict compliance requirements around information handling. Our latest models Command R / R+ are trained to provide highly-accurate in-line citations for all RAG solutions.

Command R / R+ is uniquely trained to generate accurate citations.

By leveraging RAG, businesses can enhance the scope of capabilities of their LLM solutions, delivering more accurate, relevant, and trustworthy outputs while maintaining control over the information sources and enabling customization to their specific needs and domains. It’s no wonder that Enterprise AI builders are choosing RAG systems to handle the diversity and complexity of their LLM solutions.

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Five reasons enterprises are choosing RAG