In recent conversations with friends and colleagues, I’ve noticed recurring questions about implementing large language models (LLMs) effectively. These discussions often highlight critical but overlooked aspects of working with this powerful technology. Creating a successful product starts with achieving Product-Market…
In the realm of AI advancements, OpenAI’s O1 model has emerged as a groundbreaking innovation, surpassing the reasoning capabilities of GPT-4. With its emphasis on integrating slow, deliberate thinking processes, the O1 model marks a pivotal shift in how large…
In the ever-evolving world of artificial intelligence, knowing how to maximize the capabilities of large language models (LLMs) is critical. In this blog post, we’ll explore three popular methodologies—Retrieval-Augmented Generation (RAG), in-context learning, and fine-tuning—and guide you on how to…
In the rapidly evolving field of AI, understanding the methods used to enhance and adapt large language models (LLMs) is crucial. This blog will break down three prominent techniques: in-context learning (ICL), fine-tuning, and continual pretraining (CPT). Each has unique…
Continual pretraining, particularly incremental pretraining, is a key concept in the evolution of large language models (LLMs). But when should we consider this advanced technique? Let’s explore the scenarios where incremental pretraining is necessary and its role in enhancing model…
In the fascinating world of prompt engineering, one technique stands out for its simplicity and effectiveness: in-context learning. This method involves embedding examples directly into the prompts we design, effectively activating the model’s latent abilities. Let’s explore this concept, its…
In recent conversations, I’ve noticed some curiosity around what an “end-to-end model” really means. If you’re wondering the same, let me break it down for you. Imagine tackling a complex task. A typical approach involves breaking it into multiple steps…
Currently, hallucination in large language models (LLMs) poses a significant obstacle to their deployment in industry. While efforts are underway to reduce hallucination, understanding its nature and causes is essential for improving these models. What is Hallucination? In simple terms,…
When deploying large models to provide services, the associated costs can be significantly high. This makes model compression a critical area of focus. Compression aims to reduce large models into smaller versions, thereby lowering costs and often improving inference speed.…
If you’ve explored the world of AI, chances are you’ve come across a diagram illustrating RLHF—Reinforcement Learning with Human Feedback. This process represents a cornerstone of the success behind AI models like ChatGPT. But what exactly is RLHF, and how…
