RAG Workflows and Knowledge Bases

Learning Outcome

Design grounded AI answers using approved documents and retrieval steps.

Core Ideas

• RAG: Retrieval augmented generation.
• Retriever: Component that finds relevant source chunks.
• Grounded answer: Answer based on retrieved evidence.
• Citation: Pointer to the source used.

Career Use Case

A company can answer policy questions with AI only after retrieving approved, current documents from its knowledge base.

Practical Workflow

1. Start by naming the outcome: what should improve after using RAG Workflows and Knowledge Bases?
2. Add the input material, constraints, and success criteria before asking for output.
3. Ask for assumptions and uncertainty when the answer affects a real decision.
4. Verify important claims, numbers, and policy statements before publishing or acting.

Hands-On Mini Task

• Sketch a RAG answer flow: user question, retrieval, source ranking, answer, citation, and fallback.
• A good RAG workflow refuses or escalates when sources are missing or stale.
• Before moving on, explain how RAG and Retriever change the decision.

Common Mistakes

• Using a generic prompt when the task needs clear context.
• Accepting polished wording as proof of accuracy.
• Sharing private data without redaction or approval.
• Skipping a final human review for important decisions.

Quick Revision

Module 7: LLMs, RAG And Agents lesson 20 is about practical judgement: use AI to increase speed, but keep the goal, context, evidence, and accountability clear.

FAQs