In this blog, we will break down common practical challenges encountered when building applications with Retrieval-Augmented Generation (RAG) workflows. While RAG can streamline large-model applications, real-world implementation requires careful handling of document types, chunking, embedding, retrieval accuracy, and more. From…
In this blog, we will dive into the essential concepts of full fine-tuning and LoRA fine-tuning, two popular techniques in the model adaptation space. I’ll break down what model fine-tuning really means, why we use it, and how LoRA enables…
In this blog, we will dive into the concept of “catastrophic forgetting” in LLM model training and explore how it impacts the abilities of models during re-training. We compare full fine-tuning and LoRA (Low-Rank Adaptation) methods, discussing their applications, benefits,…
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,…
The field of large language models (LLMs) has advanced rapidly over the past few years. However, there’s a world of difference between a cool demo and a reliable, scalable LLM product. In this article, I’ll blend lessons from the practices…
Building and deploying Large Language Model (LLM) applications for production environments in 2024 involves selecting the right technologies, frameworks, and tools. Based on our experience working on various LLM-based projects—from Retrieval-Augmented Generation (RAG) to agent-driven systems—this guide walks through the…
