[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-retrieval-augmented-generation::en":3,"gloss-cluster-retrieval-augmented-generation::en":23,"gloss-next-retrieval-augmented-generation::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"retrieval-augmented-generation","core-ai","Retrieval-Augmented Generation (RAG)","Retrieval-Augmented Generation (RAG) is an architecture pattern that combines a search\u002Fretrieval step with an LLM's generation step so the model answers using your specific, current data instead of relying solely on what it memorized during training. This solves two core LLM weaknesses: hallucination (making up plausible-sounding but false facts) and staleness (training data has a cutoff date). RAG matters enormously for SaaS builders because it's the cheapest, fastest way to make a general-purpose LLM behave like a domain expert on your product, documentation, or customer data — without the cost and complexity of fine-tuning. The flow has three steps. First, retrieve: convert the user's question into an embedding vector, then run a similarity search against a vector database (like pgvector or Pinecone) holding embeddings of your document chunks, returning the top-k most relevant passages. Second, augment: insert those passages into the LLM's context window alongside the user's question, typically in the system prompt or as a preamble. Third, generate: the LLM produces an answer constrained to reference the supplied context. A concrete worked example: a user asks a SaaS help-bot \"How do I reset my API key?\" The system embeds that question, searches a vector store of your help-center articles, retrieves the top 3 chunks (e.g., the \"API Keys\" and \"Security Settings\" docs), and sends the LLM a prompt like: `\"Using only the context below, answer the user's question. Context: [API Keys doc excerpt]... Question: How do I reset my API key?\"` The model replies with the exact reset steps from your docs instead of guessing. RAG pipelines also typically include reranking (a second, more precise relevance pass on retrieved chunks) and citation tracking so answers can link back to source documents. RAG is not a silver bullet — retrieval quality caps generation quality, so chunking strategy, embedding model choice, and metadata filtering matter as much as the LLM itself. RAG systems fail in predictable ways builders should design around: if the retrieval step returns irrelevant chunks, the generation step confidently answers from the wrong context (garbage in, confident garbage out); if a document isn't chunked sensibly (splitting a table or a numbered procedure mid-way), retrieved context can be incomplete or misleading even when the right document is found. Production RAG pipelines typically add a reranking pass after initial retrieval, track which source chunks were actually used in each answer (for citation and debugging), and set an explicit \"I don't have enough information to answer that\" fallback when retrieved relevance scores fall below a threshold, rather than letting the model guess.","RAG grounds an LLM's answers by retrieving relevant documents from your own data and injecting them into the prompt before generation.",null,[11,14,17,20],{"slug":12,"name":13},"embedding","Embedding",{"slug":15,"name":16},"grounding","Grounding",{"slug":18,"name":19},"semantic-search","Semantic Search",{"slug":21,"name":22},"vector-database","Vector Database",[24,28,32,36,39,42,45,48,51,54,57,60],{"slug":25,"category":5,"name":26,"updated_at":27},"agentic","Agentic AI","2026-08-24T02:46:36+00:00",{"slug":29,"category":5,"name":30,"updated_at":31},"alignment-tax","Alignment Tax","2026-08-24T02:46:37+00:00",{"slug":33,"category":5,"name":34,"updated_at":35},"artificial-intelligence","Artificial Intelligence (AI)","2026-08-24T02:46:38+00:00",{"slug":37,"category":5,"name":38,"updated_at":27},"attention","Attention",{"slug":40,"category":5,"name":41,"updated_at":35},"beam-search","Beam Search",{"slug":43,"category":5,"name":44,"updated_at":31},"benchmark-contamination","Benchmark Contamination",{"slug":46,"category":5,"name":47,"updated_at":31},"catastrophic-forgetting","Catastrophic Forgetting",{"slug":49,"category":5,"name":50,"updated_at":35},"computer-vision","Computer Vision",{"slug":52,"category":5,"name":53,"updated_at":31},"constitutional-ai","Constitutional AI",{"slug":55,"category":5,"name":56,"updated_at":27},"context-window","Context Window",{"slug":58,"category":5,"name":59,"updated_at":35},"deep-learning","Deep Learning",{"slug":61,"category":5,"name":62,"updated_at":27},"diffusion-model","Diffusion Model"]