About Retrieval Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) improves the quality of responses generated by a Large Language Model (LLM) by enriching prompts with relevant, up-to-date, and domain-specific information retrieved from enterprise data sources before the request is sent to the model. This approach enables more accurate and context-aware responses without requiring fine-tuning of the underlying LLM.

Within Siebel CRM, RAG can leverage contextual information from Siebel database records, Siebel File System attachments, Knowledge Management (KM) repositories, Oracle Cloud Infrastructure (OCI) Object Storage, and other enterprise content repositories. By grounding AI responses in an organization's own data, RAG helps users access more relevant and trustworthy information directly from their Siebel environment.

Key benefits of RAG include:

• Context-rich responses that incorporate customer-specific CRM data rather than relying solely on the LLM’s general knowledge.
• Support for both structured and unstructured data, including Siebel business records, documents, attachments, and KM articles.
• Advanced similarity search capabilities across Service Requests and related business objects through a combination of semantic search, keyword matching, and structured filtering.
• Improved answer relevance for service, sales, and administrative scenarios without the cost and complexity of model fine-tuning.
• Deployment flexibility, with support for OCI-based retrieval services in cloud environments and OpenSearch-based retrieval for on-premises implementations.