More and more, we see data pipelines being built to move and prepare data for AI use cases. To avoid being too buzzwordy, we’ll define “AI use-cases” for this article as “RAG (Retrieval Augmented Generation) applications” to provide documents for a ‘chat’-like application. The goal is to “augment” the question you will be posting to an LLM (like ChatGPT) with additional content you “retrieved,” e.g.:

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You are a helpful in-store assistant. Your #1 goal in life is to
help our employees and customers find what they need.

Some helpful context is:
{{ relevant_documents }}

Your task is to answer the question:
{{ prompt }}

Please don’t make things up. Only return answers based on the
context provided in this prompt.

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