Practical AI
The perplexities of information retrieval
Jun 19, 2024
Denis Yarats, co-founder and CTO of Perplexity, dives deep into the world of AI-driven information retrieval. He highlights the shortcomings of traditional search engines and reveals how Perplexity aims to enhance accuracy and validation. The conversation explores the challenges of integrating large language models for real-time data access and the importance of verifying generated content. Denis also shares insights on future innovations in user experience design and the potential for AI to improve decision-making by synthesizing information into actionable insights.
46:06
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Quick takeaways
- Pairing LLMs with Knowledge Graphs improves answer reliability.
- Perplexity focuses on accuracy and speed for trustworthy information retrieval.
Deep dives
Enhancing Large Language Models with Knowledge Graphs and Vector Search
Utilizing Large Language Models (LLMs) like GPT for answering queries can present challenges such as hallucinations and accuracy issues. Neo4j explores pairing LLMs with Knowledge Graphs and Vector Search to provide reliable answers. By grounding LLMs in current and contextually appropriate data, such as with citations, accurate responses can be generated, enhancing the utility of these models.
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