Retrieval-Augmented Generation (RAG)

What is Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is an AI approach that combines information retrieval with generative AI to provide responses based on relevant external information. It retrieves relevant content from a connected knowledge source and provides it to a language model as context, helping generate more accurate, relevant and grounded responses.

Key Features

• Information Retrieval: Finds relevant information from connected data sources.

• Context Enrichment: Provides retrieved information to the AI model as additional context.

• Grounded Responses: Generates answers based on retrieved information rather than relying only on model knowledge.

• Semantic Search: Uses meaning and context to identify relevant content.

• Knowledge Source Integration: Connects AI models with documents, databases and other information repositories.

• Source-Based Responses: Can provide references to the information used to generate an answer.

• Dynamic Knowledge Access: Retrieves current information from connected sources when responding to queries.

Benefits

• Improves Response Relevance: Provides answers based on information relevant to the user's query.

• Reduces Information Gaps: Enables AI systems to access external knowledge sources.

• Enhances Document Discovery: Helps users find and understand relevant information across document repositories.

• Supports Knowledge Retrieval: Makes large collections of business information easier to query.

• Improves AI Accuracy: Grounds generated responses in retrieved organizational information.

• Enables Enterprise AI: Connects generative AI with business documents and knowledge repositories.

Conclusion

Retrieval-Augmented Generation (RAG) combines information retrieval with generative AI to produce responses grounded in relevant external information. When applied to enterprise documents, it can improve information discovery, knowledge retrieval and access to organizational content.