RAG reranking for production agents: four approaches, four failure modes
The article discusses the challenges of reranking in production agents, emphasizing that many agents do not truly hallucinate but fail to retrieve the correct context. It outlines four approaches to reranking and their associated failure modes, highlighting the importance of choosing the right method for different query types. The author warns that relying solely on benchmarks can lead to significant issues in agent performance.
- ▪Most agents that hallucinate in production aren't actually hallucinating; the right context just didn't make it to the top of the retrieval window.
- ▪The choice of reranker type significantly influences the failure modes encountered during operation.
- ▪Relying on benchmarks for reranker selection can lead to problems, especially when dealing with diverse query shapes.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3956503) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Abdullah Shahin Posted on Jun 3 RAG reranking for production agents: four approaches, four failure modes #ai #rag #agents Most agents that "hallucinate" in production aren't actually hallucinating. The right context existed in the index. It just didn't make it to the top of the retrieval window. Reranking is the layer that decides whether your agent sees the answer or the noise. And the choice between reranker types shapes the failure mode you'll spend the next quarter debugging.
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