Towards Data Science
Jun 27
How to Build a Powerful LLM Knowledge Base
Use coding agents to power your knowledge base The post How to Build a Powerful LLM Knowledge Base appeared first on Towards Data Science .
How to Build a Context Layer and a Company Brain
What it actually takes to turn a company's scattered knowledge into something an LLM can reliably use — and why the demo is 5% of the work. The post How to Build a Context Layer and a Company Brain appeared first on Towards Data Science .
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Towards Data Science
Jun 26
Amplify the Expert: A Philosophy for Building Enterprise RAG
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 .
Towards Data Science
Aug 20
How to Fine-Tune an LLM: An End-to-End Guide
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.
Towards Data Science
Aug 30
Context Engineering Is Changing. Here’s What It Means for Data Scientists
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.
Towards Data Science
Aug 3
Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On
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).
Towards Data Science
Jul 18
Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform.
A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI.
Microsoft Research
Jul 30
EvoLib: Turning experience into evolving knowledge
LLMs do not get smarter just by remembering more. EvoLib turns experience into evolving knowledge, taking reusable skills and insights that help models learn and adapt across tasks long after deployment.
Towards Data Science
Aug 17
How to Perform Effective Project Management with AI
Become a more productive software engineer with LLMs The post How to Perform Effective Project Management with AI appeared first on Towards Data Science .
Towards Data Science
18h ago
Measuring the Creativity Potential of LLM Agents
The article explores whether large language model (LLM) agents can discover new ideas by examining their creative potential. It frames this inquiry through the lens of creativity, aiming to assess how LLM agents might generate novel insights or solutions. The discussion is presented as a post on Towards Data Science.
Towards Data Science
Jul 21
Prompt Engineering Isn’t Enough: How Four Bricks of Context Engineering Stop RAG Hallucinations
Enterprise Document Intelligence [Vol. 1 #9bis] - Your RAG isn’t hallucinating, it’s answering the wrong context faithfully.
Towards Data Science
Aug 3
The AI Was the Easy Part: What Is a Forward-Deployed Engineer in a Supply Chain?
What actually makes a Forward Deployed Engineer, told through one supply chain project. The post The AI Was the Easy Part: What Is a Forward-Deployed Engineer in a Supply Chain?
Towards Data Science
Jul 19
Backpropagation Explained for Beginners (Part 1): Building the Intuition
Let's discover how neural networks learn, step by step The post Backpropagation Explained for Beginners (Part 1): Building the Intuition appeared first on Towards Data Science .