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Apple Intelligence & Advanced RAG (Practical AI #275)

Jun 25, 2024
45:08
Snipd AI
An impromptu discussion on AI in enterprise leads to exploring Apple Intelligence implications. The debate on closed vs open models in AI and concerns in enterprise API usage are highlighted. Advanced RAG techniques for generative AI models and document retrieval enhancement are thoroughly explored.
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Podcast summary created with Snipd AI

Quick takeaways

  • To optimize Large Language Models (LLMs) like GPT, feeding them with reliable real-time data grounded in the right context is critical, Pairing LLMs with Knowledge Graphs and Vector Search shows promise in enhancing performance and accuracy.
  • Organizations must balance the allure of large language models with the practical realities of costs, data privacy, and operational efficiency, highlighting the need for a multi-model future in AI adoption.

Deep dives

The impact of Large Language Models on AI development

Large Language Models (LLMs) like GPT have brought about excitement in the AI community, but their performance can vary due to issues like hallucination. To optimize LLMs, feeding them with reliable real-time data grounded in the right context is critical. Pairing LLMs with Knowledge Graphs and Vector Search, as explored by Neo4j, shows promise in enhancing performance and accuracy by ensuring accurate connections and answers.

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