Modular RAG: Essential for AI in 2024?

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AI Summary

• Introduction to AI in 2024
  • Focus on building new AI worlds outside the main AI system.
  • Emphasis on retrieval-augmented generation (RAG) systems.
• AI Building Blocks
  • Pre-training, fine-tuning, and DPO alignment of LLMs.
  • Prompt engineering and context learning.
• retrieval-augmented-generation-overview (RAG)
  • Evolved from 2023’s retrieval-augmented generation system to a more complex system.
  • Introduction of expression language L cell for no-code AI prototype production.
• RAG System Complexity
  • RAG systems can grow and combine to form meta-RAG systems.
  • RAG can answer complex queries by combining various ai-tools and databases.
• Expression Language and Practical Examples
  • Expression language allows querying databases like SQL.
  • RAG can be used to analyze trends in renewable energy technologies.
• Financial Considerations
  • Investment in RAG systems can be significant.
  • Companies should be cautious about over-designed systems with multiple AIs.
• AI Integration in Companies
  • AI is not a fix-all solution for company database coherence.
  • Companies should consider optimizing internal systems before AI integration.
• Investment in AI Startups
  • Significant investments in AI startups in 2023.
  • Investors expect high returns, leading to a proliferation of AI solutions.
• RAG and Vector Stores
  • RAG systems can include vector stores for high-dimensional mathematical space construction.
  • RAG can perform semantic similarity searches using vector embeddings.
• RAG Development in 2023
  • From naive RAG to advanced RAG with pre- and post-retrieval optimizations.
  • Introduction of self-RAG, a self-reflective and iterative RAG system.
• RAG vs. Fine-Tuning LLMs
  • Comparison of RAG systems and fine-tuning LLMs for specific applications.
  • Considerations for choosing between RAG and fine-tuning based on business needs.
• Future of AI and RAG
  • Continuous development of RAG systems and their applications.
  • Alternatives to Nvidia GPUs, such as Apple’s MLX framework.
• Conclusion
  • AI in 2024 involves complex RAG systems and expression languages.
  • Companies should critically assess their needs before investing in AI systems.