Token Saving, and Caveman
The article discusses the evolution of token saving and compression in AI, particularly focusing on the Caveman tool. It highlights the historical context of token usage and optimization, noting how earlier models required careful management of token counts due to costs and performance issues. As AI models have advanced, the approach to token usage has shifted towards providing more detailed prompts for improved results.
- ▪Token saving and compression have become relevant again as automation accelerates and token usage rises.
- ▪Earlier AI models required strict management of token counts due to high costs and slow generation speeds.
- ▪Using formats like Markdown instead of JSON or XML significantly reduces token counts and improves response times.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3772700) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Kendrick B. Jung Posted on May 26 • Originally published at sonim1.com Token Saving, and Caveman #ai #llm #nlp #tooling Token Saving, and Caveman Introduction Caveman is getting a lot of hype these days. From blog posts and introductions, I first thought it compressed tokens down to the level of primitive “ooga booga” language. After using it for a few days, though, that was not really the case.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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