Context Engineering - Part 1- Introduction

Welcome to the first video in my Context Engineering series! 🚀 In this episode, we break down one of the most critical — and often misunderstood — aspects of building LLM-powered applications: context. We’ll start by understanding what a model’s context window really is, then explore why things go wrong when context grows too large or too noisy: 🧠 Context Explosion – when your conversation history and tool outputs overflow the model’s window ☠️ Context Poisoning – when irrelevant or harmful data sneaks in and corrupts model behavior 🎭 Context Distraction – when too many signals compete for attention, diluting the right answer 🤯 Context Confusion – when overlapping facts cause the model to get inconsistent or hallucinate ⚔️ Context Clash – when new information contradicts old, breaking coherence Finally, I share practical strategies to mitigate these problems — from smart tool loadouts to pruning, summarization, and offloading — so your LLM apps stay accurate, cost-efficient, and reliable. If you want to build AI agents that think clearly and act intelligently, this is the perfect starting point. Watch now and level up your context game!