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As generative AI models grow their context windows, a hidden problem emerges: more information often leads to worse answers. Known as context rot, this phenomenon reveals a U-shaped performance curve where accuracy peaks at moderate context sizes, then degrades as signal is buried in noise. Bigger memory doesn’t guarantee better reasoning—effective context does.

Is your AI chatbot a sleek machine or a Frankenstein monster? Too many RAG pipelines are built on "vibes," stitching together complex features without proof they actually work. It’s time to replace the guesswork with science. Learn how to forge a "Golden Dataset," deploy LLM-as-a-Judge metrics, and ruthlessly prune your bloated architecture. Stop engineering monsters and start building lean, accurate systems backed by hard data.

Is JSON bloating your LLM costs? Discover TOON (Token-Oriented Object Notation), the high-efficiency alternative designed specifically for Generative AI. By decoupling schema from data, TOON can slash token usage by nearly 50% compared to standard JSON. Perfect for RAG pipelines, lowering API latency, and maximizing context windows. Check out our head-to-head code benchmark to see exactly how much syntax overhead you can eliminate today.

Explore the rise of language models from GPT-2 to GPT-4 and their game-changing impact on AI and industry.