Enhancing RAG with Decision-Making Agents and Neo4j Vector and Graph Chain Tools Using LangChain Templates and LangServe A length title – but the blog is well worth the read LLMs + Graph DBs are always a fun read Sourav Joshi covers how to integrate Neo4j Vector and
GENERATIVE AI
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AI-Driven Personalization and Immersive Tech Reshape Customer Experience
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Discover the #transformative #trends of 2024 that will redefine customer experience, from #AI-driven personalization to #sentiment #analytics and #immersive technologies that create memorable interactions.
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OpenAI and Sam Altman: Key Figures in AI Development
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Yeah, let’s talk about OpenAI and Sam Altman now.
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GPT-Crawler: Tool for generating LLM-ready data from websites
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The repository https://
github.com/BuilderIO/gpt-
crawler
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GPT Crawler Tool for Creating Custom AI Knowledge Bases
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The GPT Crawler repository is now trending on Github. It lets you to crawl a website and export to json file which you can use as a knowledge base in your GPT
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How generative AI image models interpret user prompts
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Write a prompt, and your painting conforms to the prompt to the best of its ability
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Fine-tuning Small AI Models for Efficient and Ethical Deployment
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The conversation highlights the value of fine-tuning small AI models for specific tasks to reduce power and computational needs, emphasizing responsible, ethical scaling in AI deployment and development. We discuss tons of other fascinating AI-related topics 🙂
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AI Hallucinations: Solutions Through Human Feedback and Uncertainty
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The interview covers AI's risk of outputting false information, which we usually refer to as "hallucinations". She highlights solutions like human-informed reinforcement learning and systems seeking clarity during uncertainties.
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AI Democratizes Knowledge Like Uber Democratizes Luxury Services
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Technologies like Uber and Instacart democratize luxury services. AI does the same to all other fields. @DynamicWebPaige of @GoogleDeepMind discusses the complexity of creating large-scale AI models powering tools like Palm 2 and Copilot, democratizing knowledge.
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Learning to Filter Context for RAG Systems
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8/ Learning to Filter Context for RAG – a method that improves quality of the context provided to the generator via two steps: 1) identifying useful context-based and 2) training context filtering models that can filter retrieved contexts at inference.