Patterns
Implementation control library
Find the implementation control behind the gap.
138 implementation controls mapped to architecture, obligations and evidence. Search the problem; open implementation depth only when you need it.
Agentic Retrieval-Augmented Generation
Agentic RAG places a reasoning AI agent in the orchestration loop of the retrieval-generation pipeline, enabling it to…
Contextual RAG with Metadata Filtering
Contextual RAG with Metadata Filtering extends the foundational RAG pattern with a rich, queryable metadata layer that…
Enterprise Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is the foundational architecture pattern that grounds Large Language Model (LLM)…
Federated Retrieval-Augmented Generation
Federated RAG enables retrieval across multiple organisationally or geographically distributed knowledge bases without…
Graph Retrieval-Augmented Generation
Graph RAG combines vector-based semantic retrieval with structured knowledge graph traversal to answer complex, relati…
Hybrid Retrieval-Augmented Generation
Hybrid RAG combines dense (semantic) vector retrieval with sparse (keyword-based BM25) retrieval to achieve substantia…
Multi-Source Retrieval-Augmented Generation
Enterprise knowledge is never stored in a single system. Policy documents live in SharePoint, technical runbooks in Co…
Multimodal Retrieval-Augmented Generation
Multimodal RAG extends the retrieval-augmented generation paradigm to knowledge corpora that include images, charts, d…
Secure Retrieval-Augmented Generation
Secure RAG is the enterprise access-control overlay that must be applied to any RAG deployment handling sensitive, con…
Streaming Retrieval-Augmented Generation
Streaming RAG extends the foundational RAG architecture to continuously ingest and index data from real-time event str…