Towards Data Science
Aug 9
How to Implement Structured Output with Local LLMs
Why use it? How to implement it?
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.
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Towards Data Science
Jun 18
Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each
Getting reliable, readable responses out of your LLM, and knowing which tool to reach for The post Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each appeared first on Towards Data Science .
Towards Data Science
Jun 15
How to Effectively Align with Claude Code
Increase productivity with your LLMs The post How to Effectively Align with Claude Code appeared first on Towards Data Science .
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 .
Towards Data Science
Aug 22
Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call)
The article explains how LoRA fine‑tuning addressed an under‑labeling issue in the SigLip model. It outlines that whether this approach is suitable depends on three specific questions. The post provides guidance on evaluating the appropriateness of fine‑tuning for your own use case.
Towards Data Science
Jul 12
RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each
Two techniques, two different problems, and why the question is not really "which one wins" The post RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each appeared first on Towards Data Science .
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
Jul 24
Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship
Enterprise Document Intelligence [Vol. 1 #8quater] - Two angles on the cascade, cost and a validation loop, backed by a real sweep of twenty local models against a hosted flagship The post Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship appeared first on Towards Data Science .
Towards Data Science
Aug 31
Your LLM Can Return Perfect JSON and Still Be Wrong
The article discusses insights gained from a deeper examination of Structured Outputs when dealing with messy, incomplete data. It highlights that even when a large language model returns perfectly formatted JSON, the content can still be incorrect. The author reflects on the implications of this observation for data science practices.
Towards Data Science
Jul 28
Backpropagation Explained for Beginners (Part 2): There Has to Be a Better Way
The idea that makes backpropagation possible. The post Backpropagation Explained for Beginners (Part 2): There Has to Be a Better Way appeared first on Towards Data Science .
Towards Data Science
Aug 20
How to Effectively Align Your Intent with Claude Code
The article titled "How to Effectively Align Your Intent with Claude Code" discusses strategies for improving proficiency with Claude Code. It offers guidance on how to align your intentions with the code to achieve better results. The post was originally published on Towards Data Science.
Towards Data Science
Sep 28
How to Make Your Own JEV Model from an Open LLM
The article explains how to transform a small open‑source Qwen LLM into a fast, single‑pass text classifier by replacing its language‑modeling head with a JEV model. It provides a step‑by‑step guide to swapping the head, enabling the LLM to perform classification tasks efficiently. The process leverages the flexibility of open‑source models to create a lightweight, high‑performance classifier.