RAG Fundamentals & Context Engineering: Grounding AI in Your Data
By Learnia Team
RAG Fundamentals: Grounding AI in Your Data
This article is written in English. Our training modules are available in multiple languages.
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
Chunking Strategies
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
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.
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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.