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Building RAG System: My Experience
In the fast-paced world of Natural Language Processing (NLP), one of the most groundbreaking advancements in recent years is the emergence of the Retrieval-Augmented Generation (RAG).
As someone working extensively in this domain, I’ve come to appreciate how RAG systems bridge the gap between static models and dynamic, real-time information needs. In this article, I will share insights into how RAG works, its benefits, and why it’s revolutionizing NLP, especially for Arabic content.
What is Retrieval-Augmented Generation?
At its core, RAG combines two powerful components:
- Retriever: A system that fetches relevant documents or passages from a large knowledge base in response to a query.
- Generator: A language model (often based on transformers) that uses the retrieved information to generate contextually relevant and accurate responses.
This hybrid approach allows RAG systems to answer questions, generate summaries, and create content grounded in factual, up-to-date knowledge — a significant step beyond traditional, standalone language models.
How RAG Works?
- Query Encoding: The user’s query is encoded into a vector representation using an embedding model like…
