What is RAG? And how to build your own RAG with your own DATA?
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What is RAG? ⎯⎯⎯⎯⎯⎯⎯⎯⎯ Don’t forget to: ♻️ 𝘙𝘦𝘱𝘰𝘴𝘵 if you found this post interesting and helpful! 💡 𝘍𝘰𝘭𝘭𝘰𝘸 me for more insights and 𝘵𝘪𝘱𝘴 on 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐀𝐈. ⎯⎯⎯⎯⎯⎯⎯⎯⎯ Are you curious about the latest AI technologies, such as information retrieval and generation? Let’s discuss Retrieval-Augmented Generation (RAG), a game-changing approach that transforms and optimises how we interact with large language models (LLMs). 🔍 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐑𝐀𝐆? Imagine accessing vast amounts of data in real-time, enhancing it with the latest insights, and generating responses that are not just accurate but contextually rich. That’s RAG in action. 🔘 𝐇𝐨𝐰 𝐃𝐨𝐞𝐬 𝐈𝐭 𝐖𝐨𝐫𝐤? ↳ 𝘋𝘰𝘤𝘶𝘮𝘦𝘯𝘵 𝘗𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨: Start by breaking down massive datasets, be it PDFs, videos, or CSV files, into manageable chunks. ↳ 𝘌𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨: These chunks are then transformed into embeddings – vectors capturing the essence of the data. ↳ 𝘙𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭: When a user query arrives, the RAG application converts the user query into embedding and fetches relevant data from a vector database based on similarities, ensuring that the information is context-specific. ↳ 𝘈𝘶𝘨𝘮𝘦𝘯𝘵𝘦𝘥 𝘘𝘶𝘦𝘳𝘺: The user’s query is enriched with the retrieved data, adding depth to the context. ↳ 𝘎𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯: Finally, an LLM generates a response that’s not only relevant but enhanced by precise data from your knowledge sources. 🔘 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: ↳ RAG bridges the gap between static data and dynamic user needs, making it a powerful tool for businesses and developers aiming for more innovative, faster, and more accurate solutions. Ready to explore how RAG can revolutionise your AI-driven applications? Cheers! Deepak Bhardwaj #AI #MachineLearning #DataScience #RAG #LLM #Innovation #TechTrends #AIinBusiness #DeepLearning #NLP