Four production pitfalls that turn RAG demos into broken chatbots
The article discusses common pitfalls that can lead to failures in Retrieval-Augmented Generation (RAG) chatbots. It highlights issues such as improper handling of question distribution, inadequate chunk sizing, and lack of observability in production environments. Solutions are proposed to improve the performance and reliability of RAG systems.
- ▪Internal demos often lead to issues when the question distribution changes in production environments.
- ▪Vector search can produce misleading results when it returns the closest chunks without ensuring they answer the user's question.
- ▪Observability is crucial for maintaining RAG systems, as unnoticed degradation can significantly impact performance over time.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3948393) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } SapotaCorp Posted on May 24 • Originally published at sapotacorp.vn Four production pitfalls that turn RAG demos into broken chatbots #ai A common pattern we see: a Series A team builds a RAG assistant, runs a 50-question internal demo, ships to production, and within two weeks the support inbox is full of "the AI gave me a wrong answer" tickets. Nothing changed between Tuesday's demo and Friday's outage. The same model, the same retrieval, the same prompt template.
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