--- title: "Getting More Out of Every AI Token" url: https://daily.dev/posts/getting-more-out-of-every-ai-token-kyglhjl0z source_url: https://engineering.monday.com/getting-more-out-of-every-ai-token type: article source: "monday Engineering" published: 2026-07-16T10:53:29.862Z updated: 2026-07-17T11:11:39.239Z tags: ["llm", "ai-agents", "finops", "context-engineering"] reading_time: 8 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Getting More Out of Every AI Token **[monday Engineering](https://daily.dev/sources/monday)** · 8 min read · 0 upvotes · 0 comments ## Summary Engineers at monday.com share three practical strategies for reducing LLM token usage and AI costs in agentic workflows. The Caveman plugin instructs models to respond tersely, cutting ~65% of output tokens. RTK is a proxy that compresses shell command output (e.g., docker ps) by 60–90% before it reaches the model. Headroom is a broader context optimization layer that compresses tool outputs, logs, files, RAG chunks, and conversation history by 60–95%, with a reversible Compress-Cache-Retrieve mechanism so the agent can fetch full details when needed. The three tools target different boundaries and can be stacked: Headroom wraps the agent, RTK handles shell output underneath, and Caveman steers model verbosity. The post also cautions that over-compression can degrade answer quality, so token graphs and answer quality should both be monitored. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: --- Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#finops](https://daily.dev/tags/finops), [#context-engineering](https://daily.dev/tags/context-engineering) [View this post on daily.dev](https://daily.dev/posts/getting-more-out-of-every-ai-token-kyglhjl0z)