RAG (Retrieval-Augmented Generation)
An architecture that grounds LLM responses in specific documents by retrieving relevant context at query time — the standard approach for knowledge-base AI.
Retrieval-Augmented Generation (RAG) solves the hallucination problem for domain-specific applications by fetching relevant documents at query time and including them in the LLM's context window. A RAG pipeline typically involves embedding documents into a vector store, retrieving semantically similar chunks when a user asks a question, and passing those chunks as context to the language model. Building production RAG systems requires expertise in chunking strategies, embedding models, reranking, and evaluation frameworks.
Typical time to job-readiness: ~6 weeks.