In this blog, we explain how top-k, top-p influence large language models by controlling token sampling probabilities, balancing randomness, and improving output consistency. For detailed information, please watch the YouTube video: What are Top-K & Top-P in LLM?: Simply Explained…
In this blog, we will discuss the often-overlooked yet essential role of benchmarking in large language model (LLM) development. A benchmark, or evaluation set, serves as a pre-defined standard to assess an LLM’s performance and is a critical first step…
In this blog, we will dive deep into the true costs associated with fine-tuning large language models (LLMs) and explain why GPU resources might not be the most crucial factor. From GPU expenses and labor costs to inference and data…
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 break down the complete two-stage process of training LLM, making it easy to understand. Starting with general pre-training using vast amounts of data, I explain how we build a foundational model and then improve it…
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,…
