Use coding agents to power your knowledge base The post How to Build a Powerful LLM Knowledge Base appeared first on Towards Data Science .
By Eivind Kjosbakken
Enterprise Document Intelligence [Vol. 1 #M1] - The thesis behind every architectural choice in this series The post Amplify the Expert: A Philosophy for Building Enterprise RAG appeared first on Towards Data Science .
By angela shi
The article "How to Fine-Tune an LLM: An End-to-End Guide" offers a practical, hands‑on walkthrough for fine‑tuning large language models in real‑world scenarios. It covers the entire process from data preparation to deployment, providing readers with actionable steps to adapt LLMs to specific tasks. The guide is aimed at practitioners looking to implement fine‑tuning in a structured, end‑to‑end manner.
By Sam Black
The article discusses how context engineering is evolving and outlines practical ways data scientists can incorporate the newest guidelines into their everyday work. It explains the importance of adapting to these changes to improve model performance and relevance. The piece offers actionable steps for integrating context engineering into typical data science workflows.
By Piero Paialunga
Enterprise Document Intelligence [Vol. 1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops).
By angela shi
A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI.
By Jiayan Yin