RAG architectures enhance #LLMs by integrating proprietary data, but maintaining data freshness poses challenges. 🤯 Continuous updates of vector embeddings ensure accuracy in AI models’ responses. ✅ The below solution uses MongoDB Atlas Stream Processing and MongoDB Atlas Vector Search to streamline the updating and searching of embeddings, simplifying the process for developers. 👇 https://lnkd.in/eNGxSp7k
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RAG architectures enhance #LLMs by integrating proprietary data, but maintaining data freshness poses challenges. 🤯 Continuous updates of vector embeddings ensure accuracy in AI models’ responses. ✅ The below solution uses MongoDB Atlas Stream Processing and MongoDB Atlas Vector Search to streamline the updating and searching of embeddings, simplifying the process for developers. 👇 https://lnkd.in/ePe8YSXx
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RAG architectures enhance #LLMs by integrating proprietary data, but maintaining data freshness poses challenges. 🤯 Continuous updates of vector embeddings ensure accuracy in AI models’ responses. ✅ The below solution uses MongoDB Atlas Stream Processing and MongoDB Atlas Vector Search to streamline the updating and searching of embeddings, simplifying the process for developers. 👇 https://lnkd.in/gdC9nZkj
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RAG architectures enhance #LLMs by integrating proprietary data, but maintaining data freshness poses challenges. 🤯 Continuous updates of vector embeddings ensure accuracy in AI models’ responses. ✅ The below solution uses MongoDB Atlas Stream Processing and MongoDB Atlas Vector Search to streamline the updating and searching of embeddings, simplifying the process for developers. 👇 https://lnkd.in/gMNHXxFA
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Seasoned professional in Software Development with a strong track record in leadership and team development, deeply committed to fostering a culture-driven environment to achieve organizational objectives
RAG architectures enhance #LLMs by integrating proprietary data, but maintaining data freshness poses challenges. 🤯 Continuous updates of vector embeddings ensure accuracy in AI models’ responses. ✅ The below solution uses MongoDB Atlas Stream Processing and MongoDB Atlas Vector Search to streamline the updating and searching of embeddings, simplifying the process for developers. 👇 https://lnkd.in/e8ZNMjjH
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Strategic Account Director @MongoDB | Real-Time Data | AI/ML, GenAI, Vector Database | Board Member | Girl Dad
RAG architectures enhance #LLMs by integrating proprietary data, but maintaining data freshness poses challenges. 🤯 Continuous updates of vector embeddings ensure accuracy in AI models’ responses. ✅ The below solution uses MongoDB Atlas Stream Processing and MongoDB Atlas Vector Search to streamline the updating and searching of embeddings, simplifying the process for developers. 👇 https://lnkd.in/eZFjxY3z
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RAG architectures enhance #LLMs by integrating proprietary data, but maintaining data freshness poses challenges. 🤯 Continuous updates of vector embeddings ensure accuracy in AI models’ responses. ✅ The below solution uses MongoDB Atlas Stream Processing and MongoDB Atlas Vector Search to streamline the updating and searching of embeddings, simplifying the process for developers. 👇 https://lnkd.in/gbNFBx4b
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RAG architectures enhance #LLMs by integrating proprietary data, but maintaining data freshness poses challenges. 🤯 Continuous updates of vector embeddings ensure accuracy in AI models’ responses. ✅ The below solution uses MongoDB Atlas Stream Processing and MongoDB Atlas Vector Search to streamline the updating and searching of embeddings, simplifying the process for developers. 👇 https://lnkd.in/dJGfeapZ
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RAG architectures enhance #LLMs by integrating proprietary data, but maintaining data freshness poses challenges. 🤯 Continuous updates of vector embeddings ensure accuracy in AI models’ responses. ✅ The below solution uses MongoDB Atlas Stream Processing and MongoDB Atlas Vector Search to streamline the updating and searching of embeddings, simplifying the process for developers. 👇 https://lnkd.in/gibfWwgF
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RAG architectures enhance #LLMs by integrating proprietary data, but maintaining data freshness poses challenges. 🤯 Continuous updates of vector embeddings ensure accuracy in AI models’ responses. ✅ The below solution uses MongoDB Atlas Stream Processing and MongoDB Atlas Vector Search to streamline the updating and searching of embeddings, simplifying the process for developers. 👇 https://lnkd.in/gpACqZT5
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RAG architectures enhance #LLMs by integrating proprietary data, but maintaining data freshness poses challenges. 🤯 Continuous updates of vector embeddings ensure accuracy in AI models’ responses. ✅ The below solution uses MongoDB Atlas Stream Processing and MongoDB Atlas Vector Search to streamline the updating and searching of embeddings, simplifying the process for developers. 👇 https://lnkd.in/gD2AkViD
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GEN AI Evangelist | #TechSherpa | #LiftOthersUp
2moMerging external data intelligently solves staleness. Simplifying processes empowers more devs to leverage AI's potential. Dan Hughes