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The Hidden Complexity of Long Methods in LLM Parsing: A Refactoring Perspective
The Hidden Complexity of Long Methods in LLM Parsing: A Refactoring Perspective
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…
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…
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…
Cleaner Code
Refactoring
Extract Method
Long Method
Primitive Obsession in RAG Pipelines: A Refactoring Journey
Primitive Obsession in RAG Pipelines: A Refactoring Journey
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…
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…
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…
Cleaner Code
Refactoring
Primitive Obsession
RAG Pipelines
The hidden cost of Change Preventers in LLM pipelines
The hidden cost of Change Preventers in LLM pipelines
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…
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…
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…
Cleaner Code
Refactoring
Extract Method
Long Method
Practical Business-Ready RAG: Advanced Insights into Real-World Implementation
Practical Business-Ready RAG: Advanced Insights into Real-World Implementation
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…
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…
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…
Should we RAG?
RAG
Complexity
factors
Part 2: The Role of RAG in Mitigating Hallucinations: Promise and Limitations
Part 2: The Role of RAG in Mitigating Hallucinations: Promise and Limitations
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…
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…
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…
Limitations
Power
RAG To The Rescue
Really Helpful?
Part 1: How modular CSS makes styling a breeze
Part 1: How modular CSS makes styling a breeze
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….
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….
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….
Why Modular ?
Structure
Complexity
Benefits
