Whenever people talk about production AI, the conversation usually starts with token costs.
And that's fair.
Inference isn't free.
But I've started wondering if we're optimizing the wrong thing.
The real cost isn't always the tokens.
It's the repeated reasoning.
Think about a typical enterprise AI assistant.
For every request, it reads:
- Internal documentation
- Product manuals
- Company policies
- API documentation
- Knowledge base articles
Most of that information changes slowly.
Yet every request asks the model to reconstruct almost the same understanding from scratch.
It's like hiring a consultant every morning and spending the first hour explaining your business before asking today's question.
The answer is new.
The understanding usually isn't.
That made me think: instead of only optimizing retrieval or reducing token counts, should we also optimize how understanding itself is reused?
This question is actually what led us to build Coalent - an open source context engineering library focused on reusing synthesized context rather than repeatedly reconstructing it from the same documents.
The goal isn't to cache answers.
It's to preserve the expensive understanding that sits between retrieval and generation, while keeping answers grounded in the latest source data.
If you're curious, Coalent is live : https://coalent.ai
I'm curious how others are approaching this.
Are you optimizing token usage, caching retrieved chunks, reusing synthesized context or taking a completely different approach?
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