Part of the Analytify BI Glossary, clear definitions of the business intelligence, analytics, and modern data stack terms that matter.
RAG (Retrieval-Augmented Generation) is an architecture pattern that augments large language models (LLMs) with a retrieval step that fetches relevant context from external knowledge sources — documentation, databases, or vector stores — before generating a response, dramatically improving accuracy on domain-specific or up-to-date queries.
Plain LLMs hallucinate. They generate plausible-sounding but incorrect answers when asked about specific facts, recent events, or proprietary information not in their training data. RAG solves this by injecting verified external context into every prompt, grounding the LLM in real, current data.
For analytics use cases — including generative BI tools — RAG is the architectural pattern that makes AI safe and useful. Instead of asking an LLM “what was Q3 revenue,” the BI tool retrieves the relevant rows from the warehouse, then asks the LLM to summarise them. The LLM never invents numbers; it only formats real data.
A typical RAG pipeline has four steps:
RAG works particularly well when paired with a semantic layer for analytics. The semantic layer constrains what data can be retrieved; RAG retrieves only governed metrics and rows; the LLM only synthesises retrieved content.
A customer asks an analytics chatbot “What was our top-performing product last quarter?” The RAG system: (1) embeds the question; (2) retrieves the top 5 rows of revenue data from a governed warehouse view via the semantic layer; (3) constructs a prompt: “Given this revenue data: [Product A: $1.2M, Product B: $980K, …]. Answer: What was the top-performing product last quarter?”; (4) the LLM responds: “Product A, with $1.2M in revenue, was your top performer last quarter, 22% higher than Product B.” Source rows can be cited alongside the answer.
2026 RAG stack components:
Foundation LLMs commonly used for the generation step.
Open-source RAG orchestration frameworks. LangChain is broader; LlamaIndex is RAG-focused.
Specialised vector databases for embeddings storage and similarity search.
Vector search inside Postgres. Popular for teams that want one database for both OLTP and embeddings.
Built-in RAG over a governed semantic layer for SaaS embedded analytics.
Re-ranking models that improve RAG retrieval quality.
Fine-tuning modifies the LLM’s weights with new training data. RAG injects external context at query time without modifying the LLM. RAG is cheaper, faster to update, and produces verifiable citations. Fine-tuning is better for style and behaviour adaptation.
Without RAG, an LLM asked about your business data will hallucinate. With RAG, the LLM only synthesises real data retrieved from your warehouse via the semantic layer, dramatically reducing hallucinations and enabling source citations.
A specialised database that stores embeddings (numerical representations of text) and supports fast similarity search via approximate nearest neighbour algorithms. Pinecone, Weaviate, Qdrant, and pgvector are the most common.
For text-heavy use cases (documentation search, support agents), yes. For structured analytics use cases like GenBI, you can RAG over warehouse rows without embeddings — using SQL to retrieve relevant data based on the question.
Retrieval quality is the bottleneck — if the wrong chunks are retrieved, the LLM answer is wrong. RAG also adds latency (typically 200-500ms for retrieval) and complexity. Strong RAG implementations include re-ranking, query rewriting, and citation validation.
Agentic RAG combines retrieval with multi-step reasoning. Instead of one retrieval-then-generation pass, an LLM agent plans queries, retrieves, evaluates, and iterates. This produces better answers on complex questions but at higher cost and latency.