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Large language models are getting smarter—but the real superpower may be how we feed them context. Instead of constantly fine-tuning weights, a growing family of techniques improves models by upgrading the inputs they see: richer instructions, reusable strategies, domain heuristics, and concrete evidence. The paper “Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models” proposes … Read more

Introduction Byte Pair Encoding (BPE) is a subword tokenization scheme that gives us the best of both worlds: compact vocabulary sizes (not the full wordlist), the ability to represent any unknown word (by falling back to subwords/characters), and meaningful shared pieces (roots, suffixes) that help models generalize. GPT-2 used a BPE tokenizer with a vocabulary … Read more

What is Supervised Fine-Tuning (SFT)? Supervised fine-tuning is a training strategy where a pre-trained language model is further refined on a carefully curated dataset of prompt-response pairs. The primary goal is to “teach” the model how to generate appropriate, contextually relevant, and human-aligned responses. Key points about SFT include: For many contemporary language models, SFT … Read more

Tokenization is a fundamental yet often misunderstood process in the realm of large language models (LLMs). Despite its crucial role, it is a part of working with LLMs that many find daunting due to its complexity and the numerous challenges it introduces. In this blog post, we will explore the concept of tokenization, its importance … Read more

Introduction This blog post dives into the fascinating world of computer vision, exploring how we can teach machines to “see” using convolutional neural networks (CNNs). This post is based on a lecture from MIT’s 6.S191: Introduction to Deep Learning course. What Does it Mean to “See”? Before diving into the technical details, let’s define “vision”. … Read more