Llm

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. The good news? There are effective, battle-tested solutions to dramatically reduce these errors. Let’s dive into the multi-layered approach that actually works. ...

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 ACE, a practical framework that treats context like an evolving playbook—something you grow, refine, and curate over time to make agents and reasoning systems measurably better. ...

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 of ≈50,257 tokens, and OpenAI’s tiktoken is a fast Rust-backed implementation you can use today. Below I explain the why, the how (intuition + algorithm), and a short hands-on demo using tiktoken. ...

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 large language models (LLMs) were designed to respond immediately to requests. This meant they applied a constant amount of computing power at “test time”—the moment you ask a question or give a command—to generate a response. This fixed compute budget restricted how deeply the model could “think” about a problem, limiting its ability to handle extremely hard or challenging tasks. Imagine if your brain only spent a fixed millisecond on every problem, no matter its complexity! ...

Introduction to MCP The Model Context Protocol (MCP) is an open standard for connecting AI assistants (like large language models) to the systems where data and tools live​ In essence, MCP aims to bridge the gap between isolated AI models and real-world data sources – think of it as a “USB-C for AI applications”, providing a universal way to plug an AI model into various databases, file systems, APIs, and other tools​. ...

The recent release of DeepSeek R1 has generated significant buzz in the AI community. While much of the discussion has centered on its performance relative to models like OpenAI’s GPT-4 and Anthropic’s Claude, the real breakthrough lies in the underlying algorithmic innovations that make DeepSeek R1 both highly efficient and cost-effective. This post explores the key technical advancements that power DeepSeek’s latest model. Model Architecture and Training DeepSeek R1 is part of a broader model ecosystem, and it’s essential to distinguish between two key models: ...

Introduction The latest Ollama update makes it easier than ever to run quantized GGUF models directly from Hugging Face on your local machine. With a single command, you can bypass previous limitations, no longer needing a separate model on the Ollama Model Hub. Step-by-Step Guide 1. Install Ollama Download and install Ollama on your computer. Once installed, the ollama command will be accessible from your command line interface (CLI). 2. Select a Model from Hugging Face ...

NotebookLM: An AI-Powered Research Assistant NotebookLM is a research assistant powered by Google’s Gemini 1.5 Pro model. It’s centred around the idea of using sources and then leveraging the power of Gemini to interact with and learn from them. Here are some of the key features that make NotebookLM such a powerful tool: 1. Versatile Source Integration NotebookLM supports a variety of source formats, including: Audio files Markdown documents PDFs Google Docs and Slides Websites YouTube videos Text notes Users can upload up to 50 sources per notebook, offering great flexibility in consolidating and analyzing diverse information. ...

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 in language models like GPT-2, and the various issues associated with it. Introduction to Tokenization Tokenization is the process of converting raw text into smaller units called tokens. These tokens can be as small as individual characters or as large as entire words or subwords, depending on the specific tokenizer being used. Tokenization is the first step in feeding text data into a neural network, making it a critical component in the performance of LLMs. ...

For part 1 refer to this: Unveiling the Secrets Behind ChatGPT – Part 1 (learncodecamp.net) Implementing a Bigram Language Model When diving into the world of natural language processing (NLP) and language modeling, starting with a simple baseline model is essential. It helps establish a foundation to build upon. One of the simplest and most intuitive models for language generation is the bigram language model. This blog post will walk you through the implementation of a bigram language model using PyTorch, explaining the key concepts, steps, and code snippets along the way. ...