TokenSaver under the hood: how we cut prompt cost by 40%

Tokens are the unit of cost, latency, and quality in an AI coding session. TokenSaver is the layer in Gencode NG that treats them like a first-class resource, not an afterthought.

Prompt optimization

We rewrite prompts on the fly to strip redundancy, collapse boilerplate, and inline only the sections of a file the model actually needs to see. On long refactoring sessions, this alone accounts for a 15–25% reduction in prompt tokens.

Context and history management

Long chats accumulate dead weight. TokenSaver prunes older turns using semantic relevance rather than raw recency, so the model keeps the parts of the conversation that still matter and drops the parts that do not.

Formatting and output control

We steer the model toward compact output formats — diffs instead of full-file rewrites, structured summaries instead of prose — which cuts response tokens by roughly another 20%. In aggregate, real-world sessions land around 40% cheaper without a measurable drop in quality.