Retrieval-Augmented Generation — the architecture pattern I implement for knowledge-grounded AI systems.
RAG is my go-to architecture for AI that needs accurate, up-to-date knowledge. I implement end-to-end RAG pipelines — document ingestion, chunking strategies, embedding generation, vector storage, retrieval with reranking, and context-aware generation. This eliminates hallucinations by grounding LLM responses in actual data. I've built RAG for internal knowledge bases, customer support bots, and document Q&A systems.