Topic: retrieval-augmented generation rag
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RAG: The Essential AI Tool Marketers Need to Know
Retrieval-Augmented Generation (RAG) enhances AI outputs by integrating targeted external data, addressing issues like hallucinations and generic responses in marketing applications. RAG's success depends on high-quality, structured data, including machine-readable inputs and precise retrievabili...
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S3: Train Search Agents Faster with Less Data
s3 is an open-source framework that simplifies RAG development by streamlining retriever model training and reducing data requirements, making LLM applications more efficient. The framework introduces a modular approach, separating search from generation and improving retrieval quality with...
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Mistral's New Code Model Beats OpenAI in Retrieval Tasks
Mistral AI launched Codestral Embed, a specialized code embedding model that outperforms competitors like OpenAI and Cohere in benchmarks, priced at $0.15 per million tokens. The model excels in retrieval-augmented generation (RAG) workflows, semantic code search, and similarity matching, with cu...
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ElevenLabs Launches AI Voice Assistants That Master Natural Conversation
ElevenLabs launched **Conversational AI 2.0**, a major upgrade to its AI voice platform, featuring advanced turn-taking algorithms and multilingual capabilities for more natural enterprise applications like customer service. The update includes a **Retrieval-Augmented Generation (RAG) system** fo...
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