AI Darwinism: Why RAG Will Never Die
The Predictable Death of RAG (According to Twitter)
Like clockwork, every time a new large language model (LLM) announces a bigger context window, the hot takes flood social media:
"RAG is dead! Just stuff everything into the 10 million token context!"
This take is not just wrong, it's idiotic.
While massive context windows are impressive, simply dumping data into them is like trying to find a specific sentence by reading an entire library.
It's inefficient and ignores the real challenge: feeding the LLM the right information at the right time.
Anyone building real-world LLM applications knows this.
The secret isn't just more context; it's smarter context.
This post introduces the concept of Context Optimization—the evolution of RAG in the era of large context windows.
You'll learn why strategically selecting and presenting relevant information is crucial for maximizing performance, minimizing costs, and building AI systems that actually work in production.
Internalize this Context Optimization mindset, and you'll understand why RAG, far from being dead, is more vital than ever.
Let's dive in.