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Practical Skills

RAG (Retrieval-Augmented Generation)

Grounding AI responses in your data, the go-to pattern for custom knowledge bases

What it is

RAG is a pattern that combines information retrieval with LLM generation to give models access to data beyond their training. The basic flow: convert your documents into embeddings and store them in a vector database, embed the user's question at query time, retrieve the most similar document chunks by vector similarity, inject those chunks into the LLM's context, and let the model generate an answer grounded in the retrieved content.

RAG solves two key problems: the knowledge cutoff (your model doesn't know about events after training), and the inability to inject proprietary data (the model doesn't know about your company's documents or database).

More sophisticated RAG adds query rewriting, reranking, hybrid search (vector + keyword), and recursive retrieval.

Why it matters

RAG is the most widely deployed AI architecture pattern for enterprise applications. Any AI product that needs to reason over custom documents, internal data, or recent information will likely use RAG. Understanding it end-to-end (from embedding models to vector stores to chunking strategies) is essential for building real AI products, not just demos.

Related concepts

Resources

What is Retrieval Augmented Generation (RAG)?
youtube.com· Clear, concise explainer of the RAG concept: why LLMs need external knowledge, how retrieval works, and where it fits in the AI stack. IBM Technology's signature clean production.
6 min
RAG from Scratch (Part 1)
youtube.com· First video in LangChain's "RAG from Scratch" series. Walks through the core pipeline: document loading, splitting, embedding, vector store, retrieval, and generation. Code-along format.
15 min
RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models
youtube.com· Compares the three main approaches to customizing LLM behavior. Helps recruits understand when to use RAG vs fine-tuning vs better prompting. Great for putting RAG in context.
10 min
What Is Retrieval-Augmented Generation (RAG)?
blogs.nvidia.com· Well-illustrated, beginner-friendly overview from NVIDIA. Covers the problem RAG solves, how it works, and why it's better than fine-tuning for many use cases. No code, pure concepts.
8 min
Retrieval-Augmented Generation (RAG)
pinecone.io· In-depth but accessible guide from the leading vector database company. Covers the full RAG pipeline, embedding models, vector stores, and retrieval strategies. Good reference for recruits who want more depth.
12 min
What is RAG?
aws.amazon.com· Clean, well-structured overview. Good at explaining the "why" (knowledge cutoffs, hallucination reduction, domain-specific knowledge) before getting into the "how."
8 min
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