So while the industry tries to force the next level of AI through sheer force – more data, larger models, more computing power – it may be overlooking a fundamental law. An increasingly complex system will inevitably become heavier, more confusing, and more prone to failure. Like everything in the universe, the development of complex systems is also subject to the principle of entropy.
The culprit is the "additive context window." With every interaction, traditional AI models are forced to re-read the entire growing conversation history, leading to exponentially growing API costs.
This isn't a small problem. A conservative simulation over 500 interactions shows the shocking reality:
Standard RAG Architecture: Consumes ~347 Million tokens.
The Last RAG (TLRAG): Consumes just ~6 Million tokens.
That's a ~98% reduction in token costs, with a break-even point against standard RAG reached after just 7 interactions.
I know, a 98% saving sounds too good to be true. But it's simple math. The cost of a traditional approach follows the logic of Cumulative Tokens = Sum of (System Prompt + (Interaction Size * Number of Turns)). With every turn, the amount of data being re-processed grows, and so do the costs—exponentially.
TLRAG's "Dynamic Workspace" architecture breaks this cycle. The cost per interaction remains linear and predictable.
It's time to stop burning money. Let's build smarter.
The Last RAG is not only better on Quality of the Interactions , Persistent in Memory , Self Growing and Modulating - But also Cheaper then any Existing LLM System on Marked.
The full, reproducible simulation is in the pitch deck see comments. See for yourself.
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