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AI supply chain term

RAG (Retrieval-Augmented Generation)

RAG is a technique that lets an AI model answer using retrieved external documents rather than only what it learned in training.

What it means

Retrieval-Augmented Generation (RAG) is a technique that lets an AI model answer questions using external, up-to-date information rather than only what it memorized during training. At query time the system retrieves relevant passages from a knowledge base, a company's documents, a database, or a search index, and feeds them into the model's context so it can ground its answer in that material and cite sources. RAG is the backbone of most enterprise knowledge assistants, because it lets organizations put their own private data behind an AI without retraining the model and reduces hallucination. In the AI supply chain it sits in the application and inference layer, combining a vector database or search system with a language model. It is a key enterprise revenue surface and a lever for making general models genuinely useful on proprietary data.

Why it matters to investors

RAG is how enterprises turn general models into products grounded in their own data, making it a major enterprise revenue surface. Value accrues to the data platforms, search and retrieval layers, and analytics vendors, such as Palantir, Snowflake, and Databricks, that sit between corporate data and the model.

Companies on this part of the chain

PalantirPLTRSnowflakeSNOWDatabricksCohere

Named to show where the term sits in the AI supply chain — research, not advice, and never a recommendation to buy or sell.

Related terms

AI agentAn AI agent is software that wraps a language model so it can use tools, call APIs and take multiple steps on its own to complete a task.Context windowA context window is the maximum amount of text, measured in tokens, that a model can consider at one time.InferenceRunning a trained model to answer requests — the recurring cost of AI in production.Fine-tuningFine-tuning is the post-training stage that specializes a pretrained model on a smaller, targeted dataset to shape its behavior.

See RAG in the live AI chain.

THE ENTITY maps every constraint onto one live model — which part is tight now, who owns it, and who gets squeezed when it moves. Plain-English reads you can check.

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THE ENTITY is an educational read on the AI supply chain — research, not investment advice. It explains how the chain works and who sits where, never price targets or buy/sell calls.