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RAG

Retrieval-Augmented Generation (RAG) connects LLMs to external knowledge sources at inference time, enabling accurate, up-to-date answers without retraining. A core pattern in production AI systems.

3 articles

Machine Learning Mastery· 6 days ago

RAG vs. Fine-Tuning for Domain Adaptation: When to Use Which

The article contrasts retrieval‑augmented generation (RAG) with fine‑tuning for domain adaptation, outlining when each method is preferable. It explains that RAG injects up‑

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Towards Data Science· 4 days ago

RAG Isn't an Agent — I Built the Layer Between Retrieval and Action

The author demonstrates that Retrieval-Augmented Generation (RAG) and agent systems are distinct by building an explicit intermediary layer that connects a retrieval module to an action module. They evaluated this architecture on nine benchmark tasks, running each through a pure RAG pipeline, a pure agent pipeline, and the combined system to compare performance

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Machine Learning Mastery· Sep 18, 2026

Build And Understand a Vector Database From Scratch in 10 Easy Steps

The article walks readers through 10 incremental steps to build a vector database from scratch in Python, covering data ingestion, indexing, query processing, and persistence. It demonstrates how to implement a simple vector similarity search engine using basic Python data structures, offering a clear, hands‑on blueprint for prototyping retrieval components before scaling to production. While the implementation is lightweight and easy to understand, it trades off performance and scalability for educational clarity, making it ideal for early-stage experimentation and learning.

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