RAG

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Featured image for Cuttlesoft's "RAG Fundamentals: What It Is and When to Use It" post showing a stylized 2D projection of a vector embedding space against a dark midnight background, with seven color-coded constellations of dots representing topical clusters in a knowledge base — squid for Pricing, aquamarine for Onboarding, pacific blue for API Docs, sand-muted for Policies, urchin pink for Support, sunbeam gold for Release Notes, and a central seafoam cluster around the query — each constellation woven together with faint intra-cluster edges that suggest the local manifold structure of embeddings, plus a clockwise perimeter loop and four diagonal connectors hinting at the broader topology of the vector space, a bright seafoam query point at the center surrounded by a soft halo and a dashed search radius, four crisp seafoam edges connecting the query to its k=4 nearest neighbors with a small monospace "QUERY" callout above and "k = 4 NEAREST" label below, visually arguing that retrieval-augmented generation works because semantically related content lands in similar positions in vector space and a similarity search reliably pulls the right chunks out of a much larger corpus.
August 19, 2025 • Frank Valcarcel

RAG Fundamentals: What Is It and When to Use It

RAG is the most common pattern for putting an LLM in front of your own data, and the most commonly misunderstood. Here is what it is, when it is the right tool, and how the pieces fit together.

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