Retrieval-augmented generation (RAG)

RAG is a popular framework in which a large language model (LLM) accesses a specific knowledge base used to generate a response. Because there is no need to retrain the foundation model, this allows developers to use LLMs within a specific context in a fast, cost-effective way. RAG apps can be used for customer service, sales, marketing, knowledge bases and more. 

With Snowflake Cortex AI, you can build and deploy LLM apps that learn the unique nuances of your business and data in minutes. And since Snowflake provides industry-leading LLMs, vector search and Streamlit app-building capabilities all in a fully managed service, you can easily create production-ready RAG apps.

Retrieval-Augmented Generation (RAG) is a technique that enhances a foundation model’s (large language model or LLMs) output by referencing an external knowledge base beyond its original training data. 

LLMs, trained on vast datasets with billions of parameters, excel at tasks like answering questions, translations and sentence completion. RAG extends these capabilities by allowing the model to access specific domains or an organization's internal knowledge without having to undergo retraining. This cost-effective approach improves the accuracy, relevance and usefulness of LLM app outputs in various contexts.

LLMs rely on static training data, which may not include the most current or organization-specific information. Without guidance on authoritative sources, LLMs may generate inaccurate or inconsistent responses, especially when faced with conflicting terminology. When uncertain, LLMs might "hallucinate" or fabricate answers. RAG mitigates these issues by providing controlled access to up-to-date, authoritative sources, resulting in more accurate and reliable responses.

For LLMs to be useful, they must provide consistently reliable, authoritative responses. RAG enables response traceability to specific references and allows for the inclusion of source citations, which enhances the transparency and trustworthiness of the generated content.

In dynamic industries, information quickly becomes outdated. RAG allows pre-trained models to access current information without expensive fine-tuning. This approach enables LLMs to incorporate real-time data from various sources, including news feeds, social media, financial reports and IoT sensors, ensuring relevance and accuracy.

RAG empowers developers with greater flexibility to create tailored, purpose-built solutions. With a security framework around RAG, app developers can allow controlled access to sensitive information, ensuring that restricted data is only used when formulating responses for authorized individuals.

RAG applications enhance employee access to proprietary information within domain-specific knowledge bases, like company intranets or internal documentation systems. These models allow employees to ask specific questions using natural language (e.g., "What's our company's parental leave policy?" or "How do I request time off?") and receive responses generated from the organization's internal knowledge base. RAG ensures more accurate, contextually relevant answers and can provide personalized information based on the requester's authorization level and role within the company.

By leveraging continuously updated market data and internal reports, RAG enhances the quality and timeliness of business intelligence activities. This allows organizations to make data-driven decisions, identify emerging trends and gain a competitive edge. RAG can synthesize information from multiple sources, providing comprehensive insights that might be overlooked in traditional analysis methods.

LLM-powered customer service chatbots enhanced with RAG can handle a wide range of tasks, including product support, issue resolution and claims processing. RAG provides real-time access to accurate, verified content, including things like up-to-date product information, order status and individual customer data. This allows chatbots to deliver highly contextual and personalized responses, improving customer satisfaction and reducing the workload on human support agents.

Public-facing RAG-enabled chatbots offer 24/7 access to marketing, sales, product or service information. These systems can quickly navigate vast knowledge bases to provide users with relevant, up-to-date information at any time. This not only improves customer experience but also reduces the volume of basic inquiries that human staff has to handle, allowing them to focus on more complex issues.

End users interact with the knowledge base, typically through a chat interface or question-answering system.

Relevant data sources are aggregated, governed and continuously updated to provide an up-to-date knowledge repository. This includes preprocessing steps like chunking and embedding the text.

A vector store maintains the numerical representation (embeddings) of the knowledge base. Semantic search is used to retrieve the most relevant chunks of information based on the users’ query.

The system embeds the user’s question and retrieves relevant context from the vector store. This context is then used to prompt an LLM, which generates a contextualized response based on both the question and the retrieved information.