🤖100 Days of Generative AI – Understanding Retrieval-Augmented Generation (RAG) in Simple Terms – Day 7🤖

This content originally appeared on DEV Community and was authored by Prashant Lakhera

If you're in the field of Generative AI, you've likely heard the term RAG. While many have tried to explain it with complex details, I'm here to break it down in simple terms.

🙋‍♂️Imagine you're asking a question through a user interface. The process behind the scenes can be broken down into three steps:

1️⃣ Text Embedding: Your question is transformed into a format (a vector) that a computer can understand.

2️⃣ Similarity Search: This vector is then compared with other stored pieces of information in a database (a vector store). If a match is found, this matching piece of information is returned as "context." In RAG terms, this step is known as Retrieval.

3️⃣ Augmentation and Generation: The context, along with your original question, is sent to a powerful AI model. This process of combining the question with context is called Augmentation. The AI model then generates a response based on both, completing the Generation step.

In essence, RAG is about using relevant information (retrieved context) to help AI models give you better, more accurate answers.

📚 If you’d like to learn more about this topic, please check out my book. Building an LLMOps Pipeline Using Hugging Face

https://pratimuniyal.gumroad.com/l/BuildinganLLMOpsPipelineUsingHuggingFace

This content originally appeared on DEV Community and was authored by Prashant Lakhera

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Prashant Lakhera | Sciencx (2024-08-13T23:38:59+00:00) 🤖100 Days of Generative AI – Understanding Retrieval-Augmented Generation (RAG) in Simple Terms – Day 7🤖. Retrieved from https://www.scien.cx/2024/08/13/%f0%9f%a4%96100-days-of-generative-ai-understanding-retrieval-augmented-generation-rag-in-simple-terms-day-7%f0%9f%a4%96/

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