RAG

RAG (Retrieval-Augmented Generation)

RAG (Retrieval-Augmented Generation) is a technique that enhances AI responses by dynamically retrieving relevant, up-to-date information from authorized data sources before generating an answer. The RAG market is projected to explode from $1.96 billion in 2025 to $40.34 billion by 2035, representing a 1,958% growth rate.

How RAG Works

Initially, the RAG architecture was explained in a straightforward way: use an LLM for reasoning and a vector database for knowledge. It quickly became clear that RAG requires more nuanced considerations such as data modeling for embeddings, permissioning for LLMs, prompt engineering, fine-tuning, and implementing AI guardrails to ensure quality and security. Data breach costs from RAG security vulnerabilities are projected to exceed $5 million annually, making robust security frameworks critical for enterprise adoption.

Initially, the RAG architecture was explained in a straightforward way: use an LLM for reasoning and a vector database for knowledge. It quickly became clear that RAG requires more nuanced considerations such as data modeling for embeddings, permissioning for LLMs, prompt engineering, fine-tuning, and implementing AI guardrails to ensure quality and security.

Without a RAG system, the LLM is prone to generating hallucinations, leaking data, and providing irrelevant responses. The RAG system guides the reasoning process to ensure LLMs generate relevant and accurate responses while maintaining data security and proper permissions.

The Three Components of RAG

Plan

In traditional search, results are optimized using factors like clicks, positioning, and time spent on a page. An assistant, on the other hand, has a single shot at providing a relevant response using limited interaction data.

Query planning teaches the LLM how to use search engines to retrieve the information necessary to answer the user's question. This includes rewriting the query to bring in enterprise-specific knowledge about available data sources and how they can be queried.

Retrieve

During retrieval, relevant information is fetched from your knowledge base and sent to an LLM. Retrieval relies on enterprise search systems that are permissions-enforced, ensuring the LLM formulates its response using only data that a user has access to. By designing permissions upstream of LLMs, organizations can effectively address the problem of data leakage.

Generate

The LLM generates a response based on the relevant context provided from the retrieval system. After the response is generated, the system reviews the response and provides citations. These citations make it easy for users to verify the results and jump into the original documents for additional context.

Why RAG Matters for Enterprise

Customer Support: RAG helps support agents quickly find relevant documentation and craft responses using company-specific knowledge and tone of voice. Notably, LinkedIn's RAG implementation achieved a 28.6% reduction in support resolution times by integrating knowledge graphs with retrieval-augmented generation.

Customer Support: RAG helps support agents quickly find relevant documentation and craft responses using company-specific knowledge and tone of voice. In industries such as healthcare, RAG systems achieve 72% faster access to relevant information and reduce treatment decision time by 35%, while financial services save $4.2 million annually in compliance research costs.

RAG bridges this gap by grounding LLMs in reliable, company-specific knowledge while maintaining security and permissions. Instead of relying on the LLM's training data alone, RAG ensures responses are based on your actual documents, policies, and institutional knowledge.

Common Use Cases

Customer Support: RAG helps support agents quickly find relevant documentation and craft responses using company-specific knowledge and tone of voice.

Engineering: Developers can get answers about internal systems, APIs, and best practices by querying documentation and code repositories.

Sales: Sales teams can access the latest product information, pricing, and competitive intelligence to respond to prospect questions.

HR: HR professionals can quickly reference policies, procedures, and employee information to answer questions accurately.

RAG vs. Traditional Search

Traditional enterprise search returns a list of documents for users to review. RAG takes this a step further by synthesizing information from multiple sources and generating a direct answer with citations.

Data Quality: RAG is only as good as the information it retrieves. Organizations need clean, well-organized, and up-to-date knowledge bases. For example, a telecom company incurred $2.3 million in service credits after RAG provided incorrect billing information from duplicate records, highlighting the critical importance of data quality.

Implementation Considerations

Data Quality: RAG is only as good as the information it retrieves. Organizations need clean, well-organized, and up-to-date knowledge bases.

Permissions: Enterprise RAG systems must respect existing access controls, ensuring users only see information they're authorized to access.

Evaluation: Organizations should implement systems to monitor and evaluate RAG responses for accuracy, relevance, and adherence to company guidelines.

FAQ

What's the difference between RAG and fine-tuning?
Fine-tuning trains a model on your data, while RAG retrieves information at query time. RAG is more flexible for frequently changing information and maintains better data security since your information isn't embedded in the model weights.

How does RAG handle data privacy?
Enterprise RAG systems enforce permissions at the retrieval level, ensuring users only access information they're authorized to see. Additionally, contractual agreements with LLM providers ensure zero-day data retention and prevent models from training on enterprise data.

Can RAG work with real-time information?
Yes, RAG can access up-to-date information since it retrieves data at query time rather than relying on static training data. This makes it ideal for dynamic enterprise environments where information changes frequently.

How accurate is RAG compared to human responses?
RAG accuracy depends on the quality of the underlying search and retrieval system. Well-implemented RAG systems can achieve near-human accuracy while providing the advantage of instant access to vast amounts of information with proper citations for verification.