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