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What's AI Podcast by Louis-François Bouchard
Jerry Liu on the Future of AI: LlamaIndex, LLMs, RAG, Prompting and more ! What's AI Podcast Episode 25
Dec 18, 2023
Jerry Liu, Co-founder and CEO of LlamaIndex, shares insights on Retrieval Augmented Generation (RAG) and LLMs in this podcast. They discuss the importance of good communication and documentation, the business aspects of LLM solutions, and the comparison of LlamaIndex with other tools. They also delve into the deep dive of RAG, the debate between RAG and fine-tuning, the importance of chunking size in RAG systems, and the relevance of prompt engineering in LLM development. The podcast concludes with a discussion on personal usage of AI tools and coding assistants.
58:50
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Quick takeaways
- RAG combines pre-trained models with data from a corpus to generate relevant answers, and advancements in NLP may reduce the need for manual prompting.
- Chunking data in RAG is crucial for retrieval performance, requiring optimized chunk size and data quality evaluation.
Deep dives
RAG: The Future of Memory-based Language Models
RAG, or retrieval augmented generation, is a popular and valuable use case for search and retrieval. It combines pre-trained models like GPT-4 with data from a corpus, indexing the data and utilizing it to generate relevant answers to user queries. RAG is easy to set up and provides efficiency in time and cost. While prompt engineering, the fine-tuning of models, and data quality are important considerations in RAG, advancements in natural language processing will likely lead to models that can understand longer context windows and require less manual prompting. While prompt engineering may evolve, the need for effective use and understanding of AI applications will remain important skills in the future.
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