Why Building a Production RAG Pipeline is Easier Than You Think

Adding AI to legacy code doesn’t have to be a challenge. Many devs are hearing this right now: “We need to add AI to the app.” And for many of them, panic ensues. The assumption is that you have to rip your existing architecture down to its foundation. You start having nightmares about standing up… Continue reading Why Building a Production RAG Pipeline is Easier Than You Think

How Even Senior Developers Mess Up Their Git Workflow

There are few things in software engineering that induce panic quite like a massive git merge conflict. You pull down the latest code, open your editor, and suddenly your screen is bleeding with <<<<<<< HEAD markers. Your logic is tangled with someone else’s, the CSS is conflicting, and you realise you just wasted hours building… Continue reading How Even Senior Developers Mess Up Their Git Workflow

How to Automate Python Performance Benchmarking in Your CI/CD Pipeline

The issue with traditional performance tracking is that it is often an afterthought. We treat performance as a debugging task, (something we do after users complain), rather than a quality gate. Worse, when we try to automate it, we run into the “Noisy Neighbour” problem. If you run a benchmark in a GitHub Action, and… Continue reading How to Automate Python Performance Benchmarking in Your CI/CD Pipeline

Case Study: Developing and Testing Python Packages with uv

Structuring Python projects properly, especially when developing packages, can often be confusing. Many developers struggle with common questions: To help clarify these common challenges, I’ll show how I typically set up Python projects and organise package structures using the Python package and environment manager, uv. The challenge A typical and recurring problem in Python is… Continue reading Case Study: Developing and Testing Python Packages with uv

Optimizing Python: Understanding Generator Mechanics, Expressions, and Efficiency

Python generators provide an elegant mechanism for handling iteration, particularly for large datasets where traditional approaches may be memory-intensive. Unlike standard functions that compute and return all values at once, generators produce values on demand through the yield statement, enabling efficient memory usage and creating new possibilities for data processing workflows. Generator Function Mechanics At… Continue reading Optimizing Python: Understanding Generator Mechanics, Expressions, and Efficiency

The Mutable Trap: Avoiding Unintended Side Effects in Python

Ever had a Python function behave strangely, remembering values between calls when it shouldn’t? You’re not alone! This is one of Python’s sneakiest pitfalls—mutable default parameters. Recently someone asked for help in our Pybites Circle Community with a Bite exercise that seemed to be behaving unexpectedly. It turned out that this was a result of modifying a mutable parameter… Continue reading The Mutable Trap: Avoiding Unintended Side Effects in Python