This article offers a comprehensive, hands-on walkthrough for AI engineers looking to move from prototype to production. It covers three critical areas: building a custom Agent framework from scratch, optimizing RAG retrieval pipelines for better accuracy and latency, and navigating common pitfalls when using Milvus as a vector database. The author shares concrete code examples and debugging strategies that are often missing from official documentation. For developers and indie hackers building AI-powered applications, this guide provides actionable insights to avoid costly mistakes and accelerate development. The content is particularly relevant for those working with large language models and needing robust retrieval-augmented generation systems.
A practical guide to building Agent frameworks, optimizing RAG, and avoiding Milvus pitfalls for production AI systems.