learn with numberz.ai

Optimizing your business with AI and ML techniques

  • The Hidden Complexity of Long Methods in LLM Parsing: A Refactoring Perspective

    Keep It Short: Taming Long Methods for Cleaner Code At Numberz.ai, we believe in building a strong relationship with our code, no matter how few lines it may be. Our guiding principles are clarity, testability, and relentless pursuit of perfection.  Most code smells are simple to spot and even easier to fix, but doing so…

  • Primitive Obsession in RAG Pipelines: A Refactoring Journey

    Break Free from Primitive Obsession: Clean Code Starts Here At numberz.ai, we believe in crafting clean, expressive, and testable code to ensure robust pipelines, especially in complex systems like Retrieval-Augmented Generation (RAG). As we tackle code smells in various stages of development, one of the most common yet subtle offenders is Primitive Obsession. Primitive Obsession…

  • The hidden cost of Change Preventers in LLM pipelines

    Code that resists change is destined to fail. Numberz.ai, we believe in building a strong relationship with our code, no matter how few lines it may be. Our guiding principles are clarity, testability, and relentless pursuit of perfection.  Most code smells are simple to spot and even easier to fix, but doing so requires unwavering…

  • Practical Business-Ready RAG: Advanced Insights into Real-World Implementation

    Unlock Business Value with Practical RAG Implementation In our previous series, we dissected the advantages of RAG (Retrieval-Augmented Generation) with a focus on its potential to mitigate hallucinations in generative models. Now, we pivot to a parallel series that takes a granular look at the RAG framework, specifically addressing the operational complexities that prevent it…

  • Part 2: The Role of RAG in Mitigating Hallucinations: Promise and Limitations 

    Accuracy: Can Retrieval-Augmented Generation (RAG) Truly Tame AI Hallucinations?  In the first part of this series, we explored what are hallucinations in Language Models (LLMs), unpacking their nature, origin, and the challenges they pose to businesses. To summarise, hallucinations are erroneous outputs generated by Language Models (LLMs) when faced with insufficient information, leading to inaccuracies…

  • Part 1: How modular CSS makes styling a breeze

    Cascading Style Sheets (CSS) are a fundamental building block of web development. But as your project grows, managing styles across multiple files can become a nightmare. Enter modular CSS, a powerful approach that keeps your styles organised, maintainable, and free from conflicts. Why Go Modular? Traditional CSS often leads to a tangled mess of styles….