I built a production RAG pipeline. Here's what most tutorials skip.

📰 Dev.to · Anurag Srivastava

Learn how to build a production-ready RAG pipeline by following the steps outlined in this article, which covers what most tutorials skip.

intermediate Published 13 Apr 2026

Action Steps

Design a RAG pipeline architecture using a combination of retriever and generator models.
Choose a suitable embedding model and configure it for your specific use case.
Implement a vector database to store and manage embeddings efficiently.
Develop a querying system to retrieve relevant information from the vector database.
Fine-tune the RAG pipeline using feedback mechanisms to improve its performance.

Who Needs to Know This

This article is relevant to machine learning engineers, data scientists, and software developers who want to build efficient and scalable RAG pipelines. The team can benefit from this article by learning how to design and implement a production-ready RAG pipeline.

Key Insight

💡 A well-designed RAG pipeline requires careful consideration of the retriever and generator models, embedding models, vector databases, and querying systems.