RAG and knowledge
Grounding and citations
Make model answers traceable to retrieved evidence.
Lesson overview
Grounding and citations
Grounding means the answer is constrained by relevant evidence. Preserve document IDs and source metadata with retrieved chunks so the final answer can cite or link back to the source.
Do not claim a source supports a statement unless the retrieved content actually supports it.
Learning path
Theory → Example → Code → Practice → Quiz → Challenge → Completion
Step 1
Theory
Grounding and citations
Grounding means the answer is constrained by relevant evidence. Preserve document IDs and source metadata with retrieved chunks so the final answer can cite or link back to the source.
Do not claim a source supports a statement unless the retrieved content actually supports it.
Step 2
Example
Example
Apply Grounding and citations to a realistic production scenario and trace the decision step by step.
Step 3
Code
Code
Add implementation notes or a runnable example for this concept.
Step 4
Practice
Practice
Write down the inputs, expected output, constraints, and one failure case for Grounding and citations.
Step 5
Quiz
Quiz coming soon.
Step 6
Challenge
Challenge
Design a production-ready solution for Grounding and citations and explain one important trade-off.
Complete every stage
Work through every step in order, then the lesson will be marked complete.
Each chapter and subtopic has its own public URL under /ai-agent.