Llm

Introduction Traditional RAG systems treat knowledge as a collection of text chunks—embedded, indexed, and retrieved based on semantic similarity. This works well for simple factual lookup, but fails when questions require understanding relationships, dependencies, or multi-hop reasoning. Graph RAG fundamentally reimagines how knowledge is represented: instead of flat documents, information is structured as a graph of entities and relationships. This enables LLMs to traverse connections, follow dependencies, and reason about how concepts relate to each other. ...

Introduction Retrieval-Augmented Generation (RAG) transformed how LLMs access external knowledge. But traditional RAG has a fundamental limitation: it’s passive. You retrieve once, hope it’s relevant, and generate an answer. If the retrieval fails, the entire system fails. Agentic RAG changes this paradigm. Instead of a single retrieve-then-generate pass, an AI agent actively plans retrieval strategies, evaluates results, reformulates queries, and iterates until it finds sufficient information—or determines that it cannot. ...

In 2017, a team at Google published a paper that would fundamentally reshape the landscape of machine learning. “Attention Is All You Need” by Vaswani et al. introduced the Transformer architecture—a bold departure from the recurrent and convolutional approaches that had dominated sequence modeling for years. The paper’s central thesis was radical: you don’t need recurrence or convolution at all. Just attention mechanisms and feed-forward networks are sufficient to achieve state-of-the-art results in sequence-to-sequence tasks. ...

This guide is a comprehensive, production-grade walkthrough for building Agent-to-Agent (A2A) systems — from first principles to real-world deployment. It is written for engineers who already understand APIs, cloud infrastructure, and LLMs, but are new to multi-agent interoperability. The focus is on practical engineering, not demos. 1. What Is Agent-to-Agent (A2A)? A2A (Agent-to-Agent) is an architectural pattern and emerging protocol standard that enables autonomous software agents to: Discover each other Advertise capabilities Exchange structured tasks Stream intermediate progress Exchange artifacts and results Operate independently across services, teams, or organizations Think of A2A as: ...

Unsloth has quickly become one of the most practical ways to fine‑tune large language models (LLMs) efficiently on modest GPUs. It wraps popular open‑source models (like Llama, Mistral, Gemma, Phi) and optimizes training with techniques such as QLoRA, gradient checkpointing, and fused kernels—often cutting memory use by 50–60% and speeding up training significantly. This guide walks you from zero to production: Understanding what Unsloth is and when to use it Setting up your environment Preparing your dataset for instruction tuning Loading and configuring a base model with Unsloth Fine‑tuning with LoRA/QLoRA step by step Evaluating the model Exporting and deploying to production (vLLM, Hugging Face, etc.) Practical tips and traps to avoid All examples use Python and the Hugging Face ecosystem. ...