# Retrieval-Augmented Generation technology overview > Live situation record from CLSTR: https://clstr.news/situations/retrieval-augmented-generation-technology-overview > Updated: 2026-08-21T22:21:25.000Z. Sources: 3. Developments: 2. Retrieval-Augmented Generation (RAG) is an AI technique used to ground large language models (LLMs) in external, authoritative, or domain-specific data. This method aims to reduce AI hallucinations and provide up-to-date information without the necessity of expensive retraining or fine-tuning. The technical workflow generally involves a multi-step pipeline: documents are ingested, broken into smaller chunks, converted into vector embeddings, and stored in vector databases. When a user submits a query, the system retrieves the most semantically relevant sections to provide context for the LLM to synthesize a response. Implementation details include the use of a retriever to source information, a ranker to prioritize results, and orchestration tools such as n8n. While semantic caching can be used to improve latency and cost by serving previous answers, it does not create a persistent model of domain understanding; if a query falls outside similarity thresholds, the reasoning process must restart. ## Timeline ### 2026-08-21: Retrieval-Augmented Generation (RAG) explained Retrieval-Augmented Generation (RAG) improves AI accuracy by connecting large language models to external data sources, reducing hallucinations and providing up-to-date, domain-specific context. 2 sources. https://clstr.news/cluster/retrieval-augmented-generation-rag-explained ### 2026-08-16: RAG Frameworks: Understanding Retrieval-Augmented Generation An overview of Retrieval-Augmented Generation (RAG) frameworks, detailing the pipeline of ingestion, retrieval, and generation, and the limitations of semantic caching in building persistent knowledge. 2 sources. https://clstr.news/cluster/rag-frameworks-understanding-retrieval-augmented-generation --- Cite as: Retrieval-Augmented Generation technology overview. CLSTR, https://clstr.news/situations/retrieval-augmented-generation-technology-overview