RAG
RAG is an AI pattern that retrieves relevant documents from a knowledge store and supplies them as context to a language model before generating a response.
Also known as: retrieval-augmented-generation
Why it matters
Raw LLMs hallucinate product details and invent metrics. RAG anchors answers in your actual docs, analytics summaries, and brand facts, the difference between trustworthy analyst output and generic chatbot noise.
How it works
A query embeds to a vector space; similar chunks retrieve from a corpus; the model receives those passages in its prompt and generates with citations or implicit grounding. Quality depends on chunking, embedding model, retrieval ranking, and source freshness.
Common mistakes
- Retrieving stale or contradictory chunks without freshness ranking.
- Chunking so large that retrieval misses precise facts.
- Skipping human review on high-stakes outputs anyway.
- Treating RAG as a substitute for connecting live analytics APIs.
Best practices
- Curate authoritative sources in the Knowledge Base.
- Refresh embeddings when pricing, features, or positioning changes.
- Combine retrieval with structured analytics for hybrid answers.
- Log retrieval sets for debugging wrong answers.
Learn Domains perspective
Ask the AI Analyst: "What's our refund policy for annual plans?" It pulls the answer from your Knowledge Base before responding, so you get your actual policy, not a plausible guess.
FAQ
- Does RAG eliminate hallucinations?
- It reduces them when retrieval is good, it does not guarantee perfection without QA and human review.
- What data does Learn Domains retrieve?
- Knowledge Base chunks, URL Library entries, and summarized connected analytics, scoped per org.
- Is RAG used on the marketing chatbot?
- The homepage chatbot handles sales and qualification with tight limits, full analyst-style retrieval is for authenticated vault work.
Next steps
- 1Populate Knowledge Base with canonical product facts.
- 2Ask the AI Analyst a question only your docs can answer.
- 3Verify the response cites the expected source slice.
Knowledge graph
Parent terms
Child terms
Related concepts
ai-growth-analyst · knowledge-base · ai-model-router