Retrieval-Augmented Generation (RAG) is a groundbreaking approach that bridges retrieval systems and large language models (LLMs). However, while the concept is elegant, its practical implementation faces structural challenges. In this blog, we’ll explore RAG’s architecture, identify its core issues, and…
In this blog, we dive deeper into the practical application of RAG (Retrieval-Augmented Generation) and fine-tuning by exploring real-world scenarios. If you’re deciding between these approaches for your AI solutions, this breakdown will clarify which to use based on specific…
When building applications with large language models, the choice between RAG (Retrieval-Augmented Generation) and fine-tuning depends on the use case. Here’s a quick breakdown: RAG: Adds external knowledge to the base model without altering it, making it efficient for dynamic…
In this blog, we will guide you through the practical steps of choosing between prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning when working with large language models. Using clear examples, we’ll show how each approach can address specific challenges: refining…
In this blog, we will explain the role of vector databases and why they are essential for efficient text vector retrieval in RAG workflows. We’ll look at how vector comparisons work, the importance of similarity measures like cosine similarity, and…
In this blog, we will explore the step-by-step process of RAG (Retrieval-Augmented Generation), a powerful method to enhance large language models with custom knowledge bases. This video breaks down how RAG allows us to incorporate enterprise knowledge and private databases,…
