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Retrieval Augmented Generation (RAG) in AI
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Practical Solutions and Value:
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Retrieval Augmented Generation (RAG) enhances Large Language Models (LLMs) by referencing external knowledge sources, improving accuracy and relevance of AI-generated text. By combining LLM capabilities with information retrieval systems, RAG ensures more reliable responses in various applications.
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Architecture of RAG
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Practical Workflow:
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– RAG retrieves external data based on user query.
– Data is converted into numerical form for AI understanding.
– User query matches with data to provide accurate responses.
– RAG augments user prompt with retrieved data for better answers.
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Use Cases of RAG in Real-world Applications
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Real-world Applications:
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– Enhances question-answering systems in healthcare.
– Streamlines content creation and generates concise summaries.
– Improves conversational agents like chatbots and virtual assistants.
– Utilized in knowledge-based search systems, legal research, and education.
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Key Challenges
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Considerations:
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– Building and maintaining integrations with 3rd party data.
– Privacy and compliance issues with data sources.
– Latency in responses due to data size and network delays.
– Unreliable data sources leading to false or biased information.
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Future Trends
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Evolution of RAG:
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– Multimodal RAG handling various data types.
– Multimodal LLMs improving semantic understanding for better responses.
– Widening AI applications in healthcare, education, and legal research with advanced models.
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Evolve Your Company with AI
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Advancing with AI:
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