Nitin Introduction If you’ve ever stared at a cryptic error message from a CLI tool wondering “What HTTP requests is this thing actually making?”, you’re not alone. Whether it’s a failed git clone, a mysterious npm install error, or tracking claude code for finding prompts, finding out what data your application sends to third-party services, understanding … Read more
Hallucinations in RAG (Retrieval-Augmented Generation) chatbots can undermine user trust and lead to misinformation. In this comprehensive guide, we’ll explore proven strategies to minimize these AI-generated inaccuracies and build more reliable chatbot systems. If you’re building a RAG chatbot, you’ve likely encountered the frustrating problem of hallucinations—when your AI confidently provides incorrect or fabricated information. … Read more
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 When training large language models (LLMs) the most important question is simple: how do we measure whether the model is doing well? For regression you use mean squared error, for classification you might use cross-entropy or hinge loss. But for LLMs — which predict sequences of discrete tokens — the right way to turn … Read more
Introduction In the attention mechanism used by Large Language Models (LLMs) like transformers (e.g., GPT), the core idea is to allow the model to dynamically focus on relevant parts of the input sequence when generating or understanding text. This is achieved through a process called scaled dot-product attention, where input tokens (e.g., words or subwords) … Read more
Introduction Token embeddings (aka vector embeddings) turn tokens — words, subwords, or characters — into numeric vectors that encode meaning. They’re the essential bridge between raw text and a neural network. In this post, below we will run a small demos (Word2Vec-style analogies, similarity checks), and provide concrete PyTorch code that demonstrates how an embedding … 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
Introduction In this blog post, we dive deep into tokenization, the very first step in preparing data for training large language models (LLMs). Tokenization is more than just splitting sentences into words—it’s about transforming raw text into a structured format that neural networks can process. We’ll build a tokenizer, encoder, and decoder from scratch in … Read more
Ever wondered how advanced AI models can tackle truly complex problems with a depth of analysis that seems to mimic human thought? The secret lies in a groundbreaking capability known as “thinking.” This fascinating development is designed to unblock key bottlenecks on the path to greater intelligence in AI. Moving Beyond Fixed Compute Historically, powerful … Read more
If you’re displaying images stored in an AWS S3 private bucket using signed URLs, you might have encountered a confusing scenario: images display perfectly in your web pages but throw CORS errors when you try to download them using JavaScript’s fetch API. Let’s break down the issue and explore a practical solution. Understanding the Problem … Read more
