Understand the Generated Intent Before we inspect line one for bugs, we need to answer a quieter question: what was this AI-generated code trying to do in the first place? That matters because code review is not only about spotting mistakes; it is about checking whether the implementation matches the

Identify Access Patterns First Imagine we’re sketching a database for an app before the first user ever logs in. It is tempting to start with tables, columns, and shiny diagrams, but the real starting point is more human than that: we need to understand how the system will reach for

Master Joins and Filters When you first start stitching tables together, SQL joins can feel a little like trying to match two decks of cards in your head. One table might hold customers, another might hold orders, and your job is to ask SQL to line up the right rows

Pick the 1.5B Base Model Before we touched training settings, we had to answer a more human question: which 1.5B base model should carry the project? That choice mattered more than it first seemed, because QLoRA keeps the pretrained weights frozen and learns task-specific changes through small LoRA adapters on

Start With Business Questions Before anyone opens a dashboard or writes a query, the real work has already begun: we need to know what decision we are trying to make. That is the heart of data-driven decisions. A spreadsheet full of numbers can feel impressive, but without a clear business

Why Transformers Replaced RNNs If you’ve ever watched a sentence unfold one word at a time, you already have the right picture for how recurrent neural networks, or RNNs, used to work in NLP. An RNN reads text in order and carries a running memory forward, which sounds natural at

The Limits of Bigger Models When we first meet large language models, it is tempting to think they grow wiser in the same way a library grows wiser: add more books, and knowledge keeps expanding. That is the promise of pre-training scale, the stage where a model learns from enormous

Understanding Context Memory Basics Context memory is what keeps AI customer service from feeling like a string of disconnected replies. If you have ever wondered, How does an AI remember what I already told it? you are asking the right question, because that tiny ability is what turns a chatbot

Context Memory Basics How does conversational AI remember what a customer said three messages ago without making them repeat everything? That is the heart of context memory, and it is what makes a support chat feel continuous instead of starting over every time the user adds one more detail. In