Getting Started With AI / AI Foundations
RAG, Knowledge and Grounding
Understand retrieval-augmented generation, common RAG patterns, and how grounding reduces unsupported answers.
Understand retrieval-augmented generation, common RAG patterns, and how grounding reduces unsupported answers.
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Executive note
The Core Idea
Retrieval-augmented generation, usually called RAG, is a pattern where an AI system retrieves relevant information before asking a model to answer.
Instead of relying only on what the model learned during training, the system gives it fresh, approved, task-specific context: policy documents, research notes, product manuals, previous cases, contracts, standards, or database results.
RAG is useful because enterprise answers often need current internal knowledge, not just general language ability.
Section 2 of 5
Why Grounding Matters
Grounding means tying an answer to evidence. A grounded answer should make it easier to see where the information came from and whether it is appropriate to use.
Good grounding helps reduce:
Grounding does not make a system automatically correct. It gives the workflow better raw material and a better audit trail.
- Unsupported claims.
- Stale answers.
- Answers based on the wrong source.
- Confident summaries of weak evidence.
- Leakage across permissions or business boundaries.
Section 3 of 5
Common RAG Patterns
There are several RAG types. The right choice depends on the data and decision.
The more powerful the pattern, the more governance it needs.
- Basic document RAG: retrieve chunks from documents and answer with citations.
- Hybrid RAG: combine keyword search and vector search so exact terms and semantic meaning both matter.
- Filtered RAG: restrict retrieval by user, role, region, desk, document status, date, or sensitivity.
- Structured RAG: retrieve from tables, databases, APIs, or knowledge graphs rather than only documents.
- Agentic RAG: let an agent plan multiple retrieval steps, compare sources, ask follow-up questions, or call tools.
- Graph RAG: use relationships between entities, documents, people, systems, or concepts to improve retrieval and explanation.
Section 4 of 5
What Can Go Wrong
RAG failures are often workflow failures, not model failures.
Watch for:
Recommended practice:
- Documents split into chunks that lose meaning.
- Retrieval returning plausible but irrelevant material.
- Missing freshness checks.
- No permission filtering.
- Too much context, causing the model to ignore the important parts.
- No source shown to the user.
- Start with a small trusted corpus.
- Test retrieval before testing generation.
- Evaluate whether the right sources are found.
- Show citations or evidence where the user needs trust.
- Stop when evidence is missing instead of forcing an answer.
Section 5 of 5
Remember This
RAG is not just "chat with documents." It is an evidence design pattern. The goal is to help the model answer from the right knowledge, at the right time, for the right user, with enough traceability to support review.