How seven load-bearing principles across chat sessions and agentic pipelines, keep LLM dev costs manageable without degrading what the tools produce.
Occasinally I had a problem most people working with LLMs eventually run into: Long sessions forgot their own constraints. Multi-file investigations dumped thousands of tokens into the main context and never gave them back. Pipelines paid full price for content that should have been cached. None of it was the model’s fault, all of it needs only changing how you worked with the tools. The patterns below come experience and research about LLM assisted development to scale. Design choices about where to spend tokens and where not to.
One-line thesis: token efficiency is a design discipline, effectivness not being cheap.
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