The RAG Pipeline
How RAG Works: 5-Step Pipeline
From question to grounded answer
Step 1, Document Ingestion
💡 Chunk at paragraph or section boundaries. Overlap by 50-100 tokens for continuity.
Chunking Strategies
Chunking Methods Compared
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Why is chunk overlap important in RAG?
The Context Engineering Challenge
Limitations
- →Retrieval quality bottleneck, If the wrong chunks are retrieved, the answer will be wrong or incomplete.
- →Embedding mismatch, Short queries may not embed close to long document passages. Use query expansion to improve recall.
- →Stale data, If documents change, you must re-embed. Set up automated re-indexing.
- →Multi-hop reasoning, Standard RAG retrieves chunks independently. Questions requiring information from multiple documents need advanced techniques (graph RAG, iterative retrieval).
- →Cost at scale, Embedding millions of documents and storing vectors has infrastructure costs.
Test Your Understanding
RAG Fundamentals Knowledge Check
0 / 5 answered
Q1.What does RAG stand for?
5 questions remaining
Next Steps
You now understand RAG fundamentals. In the next workshop, you will build a mini RAG system from scratch, loading documents, creating embeddings, searching, and generating grounded answers.
- →Contextual Retrieval: Advanced RAG, The contextual enrichment technique that reduces errors by 67%
Continue to the workshop: Build a Mini RAG System to put theory into practice.
Workshop Overview
What We Are Building
A document Q and A system in 5 steps
Step 1, Load Documents
// Pseudocode for the workshop
const docs = loadDocuments('./knowledge-base/')
console.log('Loaded', docs.length, 'documents')💡 Start small: 5-10 documents. You can scale later.
Tuning Your RAG System
Embedding Models for RAG
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Why does accuracy decrease when K (number of retrieved chunks) is too high?
Evaluation: How Good Is Your RAG?
Common Issues and Fixes
RAG Troubleshooting Guide
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Scaling Beyond the Workshop
Test Your Understanding
Mini RAG Workshop Check
0 / 5 answered
Q1.What are the 5 steps to build a RAG system?
5 questions remaining
Continue Learning
You have built your first RAG system. In the next module, you will learn about AI Agents, systems that can reason, use tools, and take actions autonomously using the ReAct framework.
Continue to ReAct Method: AI Agents Explained to understand autonomous AI systems.