RAG and knowledge
The retrieval pipeline
Understand ingestion, indexing, retrieval, ranking, filtering, and generation.
Lesson overview
The retrieval pipeline
A practical pipeline is ingest → normalize → chunk → index → retrieve → filter → rerank → construct context → generate.
Measure retrieval independently from answer quality so you can identify whether a failure came from missing knowledge or bad generation.
Learning path
Theory → Example → Code → Practice → Quiz → Challenge → Completion
Step 1
Theory
The retrieval pipeline
A practical pipeline is ingest → normalize → chunk → index → retrieve → filter → rerank → construct context → generate.
Measure retrieval independently from answer quality so you can identify whether a failure came from missing knowledge or bad generation.
Step 2
Example
Example
Apply The retrieval pipeline 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 The retrieval pipeline.
Step 5
Quiz
Quiz coming soon.
Step 6
Challenge
Challenge
Design a production-ready solution for The retrieval pipeline 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.