RAG(Retrieval-Augmented Generation) Demystified: A Question-First Guide for Software Developers
This article explains Retrieval-Augmented Generation (RAG), an architectural approach that combines a language model with an external knowledge retriever to provide up-to-date information to the model at runtime.
Why it matters
RAG represents an important architectural advancement for language models, allowing them to stay up-to-date and relevant in a constantly changing world.
Key Points
- 1RAG allows language models to access external information at runtime, similar to how developers look up information online when debugging
- 2RAG was invented to solve the problem of language models having stale, build-time knowledge that doesn't reflect the latest changes in the real world
- 3RAG can be understood as a combination of search, database, and prompt engineering techniques working together
Details
The article breaks down how RAG works step-by-step, explaining that it takes a query, converts it to an embedding, performs a vector store lookup to retrieve relevant documents, injects those documents into the language model's prompt, and then generates the final output. It emphasizes the importance of separating retrieval bugs from generation bugs when debugging a RAG system. The article also discusses how RAG represents a shift from hard-coding knowledge into language models to dynamically retrieving information at runtime, similar to how software development moved from hard-coded configurations to external data sources.
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