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Optimizing Retrieval-Augmented Generation (RAG) with Serverless and Sidecar Architecture
In the world of Generative AI, Retrieval-Augmented Generation (RAG) workflows have emerged as a powerful paradigm. By combining external knowledge retrieval with generative language models, RAG bridges the gap between static AI models and dynamic, contextually rich applications. However, implementing scalable, efficient, and secure RAG pipelines presents unique challenges. Enter the architecture that leverages both…
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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…
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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…
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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…
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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…
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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…