RAG Fundamentals: Grounding AI in Your Data
By Dorian Laurenceau
Updated
📅 Last reviewed: April 24, 2026. Updated with April 2026 findings and community feedback.
RAG Fundamentals: Grounding AI in Your Data
LLMs know a lot, but they do not know YOUR data. Company documents, product databases, internal wikis, customer records, none of this exists in the model's training data. RAG (Retrieval-Augmented Generation) solves this by retrieving relevant documents and injecting them into the prompt, giving the AI grounded, accurate, up-to-date answers.
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
Question 1 of 5.
RAG Fundamentals Knowledge Check
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Q1.What does RAG stand for?
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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.
Module 5 — RAG (Retrieval-Augmented Generation)
Ground AI responses in your own documents and data sources.
Dorian Laurenceau
Full-Stack Developer & Learning DesignerFull-stack web developer and learning designer. I spent 4 years as a freelance full-stack developer and 4 years teaching React, JavaScript, HTML/CSS and WordPress to adult learners. Today I design learning paths in web development and AI, grounded in learning science. I founded learn-prompting.fr to make AI practical and accessible, and built the Bluff app to gamify political transparency.
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What will I learn in this RAG guide?+
Understand Retrieval-Augmented Generation (RAG) from the ground up. Learn how to combine vector search with LLMs to build AI that answers questions from your own documents.