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Why RAG Applications Fail in Production
Artificial Intelligence   Data Science   Latest   Machine Learning

Why RAG Applications Fail in Production

Last Updated on March 25, 2024 by Editorial Team

Author(s): Dr. Mandar Karhade, MD. PhD.

Originally published on Towards AI.

It worked as a prototype; then all went down!

Retrieval-Augmented Generation (RAG) applications have emerged as powerful tools in the landscape of Large Language Models (LLMs), enhancing their capabilities by integrating external knowledge. Despite their promise, RAG applications often face challenges when transitioning from prototype to production environments. This article delves into the intricacies of RAG applications, exploring common pitfalls and strategic insights for successful deployment.

Deploying RAG applications in a production setting is fraught with challenges. The complexity of integrating generative LLMs with retrieval mechanisms means that any number of elements can malfunction, leading to potential system failures. For instance, the scalability and robustness of the system are crucial; it must handle unpredictable loads and remain operational under high demand. Moreover, predicting user interactions with the system in a live environment is challenging, necessitating continuous monitoring and adaptation to maintain performance and reliability​.


Based on Retrieval Method: RAG models can be categorized by the retrieval method they use, such as using BM25 (a traditional information retrieval function) or more advanced dense retrievers that leverage neural network-based embeddings to find relevant documents. The choice of retriever impacts how well the model can fetch pertinent information from a corpus​

Based on Generation Mechanism: The generative component of RAG usually employs transformer-based models… Read the full blog for free on Medium.

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