Each token you send costs money, consumes context and can dilute the answer. After building several AI products, I became focused on token efficiency and built LCC (Local Context Compiler) to turn that focus into a repeatable tool.
A typical support prompt contains a system instruction, conversation history, retrieved context and the user's latest message. At scale, more than half of that is redundant boilerplate, repeated headers or irrelevant chunks. LCC applies four reductions.
Whitespace normalization strips blank lines, trims trailing spaces and collapses empty runs. Structural deduplication keeps one copy when the same paragraph appears in overlapping RAG chunks. Abbreviation compression replaces repeated long phrases with shorter tokens only where meaning stays intact. Token measurement counts the exact cost of each block with tiktoken.
Compression is not free to reason about. LCC reduces raw token volume by 35 percent to 45 percent in production systems without measurable degradation in answer quality. The range depends on how redundant the input is; a clean document gains little.
Score messages by relevance, recency and the current query, then keep only what fits a defined token budget instead of resending the whole history. Long conversations are trimmed to the segment that actually matters.
Context fits a 128k window, but attention degrades over long inputs. A focused 4000-token prompt with high-relevance context beats a 40,000-token one that includes everything. Less is more only when the 'less' is selected deliberately.
LCC is open source and runs locally with no API calls. It is a deterministic pipeline: text in, optimized text out. Use it as a pre-processing step before each call when budget matters.
LCC plans a session checkpoint so agents keep a clean context across turns. Look before extending the prompt further. The repository documents the design contract and the audit report for every block dropped.
Reference: LCC open-source repository documents offline, verifiable token reduction. Reference: OpenAI prompt caching for prefix-matching semantics.