Programming Tutorials

  Building applications on top of Large Language Models (LLMs) inevitably leads to a single, frustrating bottleneck: the "chatty" API response. You ask Llama 3 or Mistral for a user profile object, and instead of raw data, you get:   "Sure! Here is the JSON you requested..."   followed by a markdown code block. For a hobby project, you might hack together a Regular Expression to strip out the text. In production, this is a fatal flaw. Application logic relies on deterministic data structures, not conversational nuances. If your parser fails because the LLM decided to add a trailing comma or a polite introduction, your user experience breaks. This guide details exactly how to bypass the conversational layer in Ollama to force rigorous, parseable JSON output using Python and Pydantic. The Root Cause: Why LLMs Fail at JSON To fix the problem, we must understand why it occurs. LLMs are not databases; they are probabilistic engines designed to predict the next token in a...

  For years, Shopify developers relied on Metafields to extend the platform's data model. If you needed to add "Care Instructions" to a product, you created a Metafield. If you needed to add a "Related Blog Post," you hacked together a URL Metafield. However, with the introduction of  Metaobjects , the architecture has shifted from simple key-value storage to a true relational database model embedded within Shopify. The confusion is immediate: If I want to display a "Designer Profile" on a product page, do I use a JSON Metafield, a set of four text Metafields, or a Metaobject? This guide breaks down the architectural differences, provides a strict decision matrix, and implements a rigorous code solution for linking structured data using Liquid. The Architectural Distinction: Attributes vs. Entities To make the right choice, you must understand the underlying data modeling concept. Metafields are Attributes.  They describe a specific property of the par...