Stop Using LLMs to Audit Other LLMs: You Are Bricking Your Production Latency
The article discusses the inefficiencies of using large language models (LLMs) to audit other LLMs in production systems. It argues that this practice can lead to increased latency and resource consumption without effectively ensuring safety or governance. The author suggests a shift towards a hybrid architecture that combines probabilistic generation with deterministic governance to improve operational decision-making.
- ▪Using LLMs to validate other LLMs can significantly increase latency and resource usage.
- ▪The author proposes a deterministic governance layer to make operational decisions more efficiently.
- ▪Governance in AI systems should focus on whether actions should proceed rather than just generating the best answers.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3958146) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } VAXONI Posted on May 30 Stop Using LLMs to Audit Other LLMs: You Are Bricking Your Production Latency #ai #javascript #node #architecture Look at your modern Agentic AI stack. An agent wants to execute a tool, trigger a deployment, access a database, or call an external API. Because nobody fully trusts a probabilistic black box, many teams now use a second probabilistic black box to validate the first one. Think about what is actually happening.
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