RAG — Retrieval-Augmented Generation

The RAG Pipeline

Chunking Strategies

The Context Engineering Challenge

Limitations

1. →Retrieval quality bottleneck — If the wrong chunks are retrieved, the answer will be wrong or incomplete.
2. →Embedding mismatch — Short queries may not embed close to long document passages. Use query expansion to improve recall.
3. →Stale data — If documents change, you must re-embed. Set up automated re-indexing.
4. →Multi-hop reasoning — Standard RAG retrieves chunks independently. Questions requiring information from multiple documents need advanced techniques (graph RAG, iterative retrieval).
5. →Cost at scale — Embedding millions of documents and storing vectors has infrastructure costs.

Test Your Understanding

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%

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Continue to the workshop: Build a Mini RAG System to put theory into practice.

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Workshop Overview

Tuning Your RAG System

Evaluation: How Good Is Your RAG?

Common Issues and Fixes

Scaling Beyond the Workshop

Test Your Understanding

Next Steps

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.

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Continue to ReAct Method: AI Agents Explained to understand autonomous AI systems.