Building a RAG Pipeline

Retrieval-Augmented Generation (RAG) is one of the most practical techniques for building AI applications that need access to specific knowledge. Instead of fine-tuning a model, you retrieve relevant documents and include them in the prompt.

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What Is RAG?

RAG combines retrieval (finding relevant information) with generation (producing an answer using an LLM). It solves the key problem of LLMs: they only know what was in their training data.

RAG vs Other Approaches

Approach	Cost	Freshness	Accuracy	Complexity
Prompt only	Very low	Static	Limited	Very low
Fine-tuning	High	Snapshot	Good	High
RAG	Medium	Real-time	High	Medium
RAG + Fine-tune	High	Real-time	Highest	High

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RAG Architecture