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Retrieval-augmented generation grounds LLM responses by retrieving relevant documents from a knowledge base and passing that context to the LLM, ensuring answers are based on external factual sources rather than model parameters.

You can build a document QA system by ingesting text into the knowledge base, performing semantic search to fetch relevant passages, and generating context-aware answers with an LLM using the retrieved document context.

Yes, the Skill integrates memory stores, vector search, and LLM providers via a provider-agnostic interface, allowing you to connect different components without being locked into a specific vendor.

The best way to track sources for AI-generated answers is using built-in source tracking and topic management features, which provide auditable results by linking responses back to their originating retrieved documents.

Yes, retrieval-augmented generation is designed for domain-specific information retrieval, fetching relevant passages from ingested documents to answer queries about product documentation, policies, or customer support topics.

Semantic search for knowledge-base QA depends entirely on ingested documents, meaning it cannot answer queries outside its knowledge base scope and requires documents to be properly added and topic-tagged beforehand.