Long Context AI: What 2 Million Tokens Actually Means

Описание: Long-context AI models can now handle 2 million tokens—but what does that actually mean for real-world applications? In this deep dive, we explore the evolution from 4K to 2M token context windows, the genuine use cases vs the hype, and when you should (and shouldn't) use long context over RAG.

📚 MENTIONED IN THIS VIDEO:
• GPT-4 (8K/32K context)
• Claude 2 & 3.5 (100K/200K context)
• Gemini 1.5 Pro & 2.0 Flash (1M/2M tokens)
• Retrieval Augmented Generation (RAG)
• Vector databases
• Attention mechanisms & transformer architecture

🎯 WHO THIS IS FOR:
• AI developers choosing between RAG and long context
• Technical leaders evaluating AI architecture
• Researchers working with large document sets
• Anyone trying to separate AI hype from reality

💡 PRACTICAL ADVICE:
Start with RAG for 90% of use cases. Use long context strategically for comprehensive analysis where retrieval fundamentally can't work. Track your costs—$10/query requires a business model that supports it.

🔗 RESOURCES:
• MIT Breakthrough Tech List:
• Gemini 2.0 Flash Announcement:
• Claude Context Window Research:
• RAG Implementation Guide:

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