Large Language Models (LLMs) have demonstrated remarkable capabilities in generating human-like text, answering questions, and assisting in various domains. However, despite their power, they have notable limitations that led to the development of Retrieval-Augmented Generation (RAG). This hybrid approach enhances LLMs by integrating retrieval mechanisms, making them more accurate, relevant, and domain-specific. In this blog post, we'll explore why RAG became necessary and how it overcomes the core challenges of LLMs. Limitations of LLMs and How RAG Solves Them 1. Access to Private and Internal Data LLMs are trained on publicly available data and do not have access to private company information or sensitive data. Businesses often need AI models that can answer questions based on internal documents, proprietary knowledge, or confidential reports. How RAG Helps:
How RAG Helps:
How RAG Helps:
How RAG Helps:
1. Retrieval: Finding the Right Data Retrieval is the first and most important step in RAG. To ensure accurate responses, we need to store and retrieve data efficiently and correctly. Key Processes in Retrieval:
3. Generation: Producing a Final, Informed Response After augmentation, the LLM generates a response based on the retrieved data.
Key Takeaways from the AI Act:
Conclusion Despite the impressive capabilities of LLMs, their limitations necessitated the development of RAG. By combining retrieval with generation, RAG enables AI models to:
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Mohammad Al Rousan is a Microsoft MVP (Azure), Microsoft Certified Solution Expert (MCSE) in Cloud Platform & Azure DevOps & Infrastructure, An active community blogger and speaker. Al Rousan has over 11 years of professional experience in IT Infrastructure and very passionate about Microsoft technologies and products. Top 10 Microsoft Azure Blogs
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