Retrieval-Augmented Generation (RAG)
Combining a prompt with fresh context retrieved from a knowledge source so the model responds with grounded, specific answers.
Retrieval-Augmented Generation (RAG) pairs a prompt with retrieved context—docs, KB articles, or records—so the model answers with grounded, up-to-date information.
Teams use RAG for policy Q&A, support assistants, sales enablement, and internal search. It reduces hallucinations by anchoring responses in sourced text.
In workflows, RAG pipelines fetch relevant chunks, build a context window, then generate a response with citations. The value is specificity and trust without retraining models.
Frequently Asked Questions
What data works best for RAG?
Well-structured, up-to-date text: KBs, policies, product docs, tickets. Clean, deduplicated content improves retrieval quality.
How do I improve retrieval accuracy?
Use good chunking, embeddings tuned to domain, hybrid search (semantic + keyword), and filters by metadata.
How do I reduce hallucinations?
Limit responses to retrieved context, add citations, and enforce structured answers. Penalize unsupported content.
What about stale data?
Refresh indexes regularly and include timestamps. Add fallbacks if no fresh context is found.
Can RAG handle private data safely?
Yes, if your retrieval and generation run in secure environments with access controls. Mask sensitive fields and log access.
How do I measure RAG quality?
Precision/recall of retrieval, groundedness of responses, citation accuracy, and user satisfaction. Monitor unsupported answers.
What models suit RAG?
Models that follow instructions well with constrained context windows. Smaller models can perform well with strong retrieval.
How do I handle multi-turn queries?
Carry forward relevant history or re-retrieve each turn. Summarize context to stay within token limits.
Do I need fine-tuning with RAG?
Often no. Start with prompt+retrieval. Fine-tune only if style or domain needs go beyond what retrieval can cover.
Agentic AI
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A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.
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A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.
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