Graph RAG, Automated Immediate Engineering, Agent Frameworks, and Different September Should-Reads

Feeling impressed to put in writing your first TDS put up? We’re always open to contributions from new authors.

There’s at all times one thing thrilling and energizing within the air after we flip the calendar to September, and this yr was no exception. Positive, bidding farewell to lengthy sunny days and a barely slower tempo could make anybody a bit wistful, however not for lengthy—not when there’s a lot occurring within the ML and AI scene, so many new instruments and improvements to study, and many new expertise to develop.

We’re thrilled to share our most-read and -shared articles of the previous month in case you missed any of them (or simply need to revisit a favourite or two). Much more than typical, they symbolize the complete breadth of matters our authors cowl, from core programming expertise to cutting-edge LLM methods, so we’re sure that you simply’ll discover one thing in our September highlights to pique your curiosity. Pleased studying, and right here’s to a brand new season filled with studying and progress!

Month-to-month Highlights

• How to Implement Graph RAG Using Knowledge Graphs and Vector Databases
  Our prime learn of the month got here from Steve Hedden: a transparent and accessible step-by-step tutorial on implementing retrieval-augmented era (RAG), semantic search, and suggestions.
• Data Scientists Can’t Excel in Python Without Mastering These Functions
  There’s at all times room for one more stable Python tutorial — and Jiayan Yin’s compendium of key features for information scientists proved particularly useful for our readers.
• Python QuickStart for People Learning AI
  Extra Python! Shaw Talebi’s beginner-friendly information focuses on the programming matters you’ll have to grasp in case your finish aim is to develop customized AI initiatives and merchandise.
• Automated Prompt Engineering: The Definitive Hands-On Guide
  Enthusiastic about studying tips on how to automate immediate engineering and unlock important efficiency enhancements in your LLM workload? Don’t miss Heiko Hotz’s sensible information.

• GenAI with Python: Build Agents from Scratch (Complete Tutorial)
  Leveraging the mixed energy of Ollama, LangChain, and LangGraph, Mauro Di Pietro walked us by your complete workflow of making customized AI brokers.
• SQL: Mastering Data Engineering Essentials (Part I)
  Whether or not you’re new to SQL or may use refresher, Leonardo Anello’s complete introduction, aimed particularly at information engineers, is a strong, one-stop useful resource.
• Choosing Between LLM Agent Frameworks
  What are the tradeoffs between constructing bespoke code-based brokers and counting on the key agent frameworks? Aparna Dhinakaran shares sensible insights and suggestions on a key query.
• Analytics Frameworks Every Data Scientist Should Know
  Drawing on her earlier expertise as a marketing consultant, Tessa Xie provides information professionals useful tips about “tips on how to break down an summary enterprise downside into smaller, clearly outlined analyses.”
• Beyond Line and Bar Charts: 7 Less Common But Powerful Visualization Types
  From bump charts to round bar plots and Sankey diagrams, Yu Dong invitations us to broaden our visual-design vocabulary and experiment with less-common visualization approaches.
• 5 Tips To Make Your Resume *Really* Stand Out in FAANG Applications
  In a aggressive market, each element counts, and small changes could make a significant distinction—which is why it is best to discover Khouloud El Alami’s actionable recommendation for present job seekers.

Our newest cohort of latest authors

Each month, we’re thrilled to see a recent group of authors be part of TDS, every sharing their very own distinctive voice, information, and expertise with our group. Should you’re in search of new writers to discover and observe, simply browse the work of our newest additions, together with Alexander Polyakov, Harsh Trivedi, Jinhwan Kim, Lenix Carter, Gilad Rubin, Laurin Brechter, Shirley Bao, Ph.D., Iqbal Rahmadhan, Jesse Xia, Sezin Sezgin-Rummelsberger, Reinhard Sellmair, Yasin Yousif, Hui Wen Goh, Amir Taubenfeld, Sébastien Saurin, James Gearheart, Zackary Nay, Jens Linden, PhD, Eyal Kazin, Dan Beltramo, Sabrine Bendimerad, Niklas von Moers, Milan Tamang, Abhinav Prasad Yasaswi, Abhinav Kimothi, Miguel Otero Pedrido, Oliver Ma, Hamza Farooq, Shanmukha Ranganath, Maarten Sukel, Murilo Gustineli, Luiz Venosa, Saankhya Mondal, David Vaughn, Prasad Mahamulkar, Federico Rucci, Philippe Ostiguy, M. Sc., Anurag Bhagat, and Megan Grant, amongst others.

Thanks for supporting the work of our authors! We love publishing articles from new authors, so in case you’ve not too long ago written an fascinating mission walkthrough, tutorial, or theoretical reflection on any of our core matters, don’t hesitate to share it with us.

Till the following Variable,

TDS Workforce

Graph RAG, Automated Prompt Engineering, Agent Frameworks, and Other September Must-Reads was initially revealed in Towards Data Science on Medium, the place persons are persevering with the dialog by highlighting and responding to this story.