Why Your LLMs Keep Forgetting Everything - Context Window Explained

Explore the fundamental limitations of Large Language Models and discover practical solutions to overcome memory constraints in this 10-minute tutorial. Learn about context windows ranging from 2K to 1M tokens, understand how token limitations affect AI performance, and master context engineering techniques through real-world examples. Dive into Retrieval-Augmented Generation (RAG) implementation using OpenAI API and vector databases to give AI systems long-term memory capabilities. Follow along with hands-on demonstrations covering environment setup, the Pi digit problem for context window testing, context engineering with an apple farm example, memory management strategies, and building a simple RAG system. Gain insights into choosing the right model for specific tasks, understand RAG drawbacks, and learn best practices for optimizing LLM performance through practical lab exercises and comprehensive walkthroughs.

Syllabus

00:00 - Why is memory a big limitation in LLMs?
00:26 - Context windows: what they are and why they matter.
01:16 - How to Choose the right model for your task?
02:00 - Context engineering and the apple farm example
03:11 - How RAG works with vector databases
03:57 - One Drawback in RAG
04:30 - Lab introduction and setup
05:08 - Lab Demo - Environment setup walkthrough
05:32 - Lab Demo - Context Window The Pi Digit Problem
06:14 - Lab Demo - Context Engineering
07:22 - Lab Demo - Memory Management
08:19 - Lab Demo - Simple RAG System
09:52 - Key takeaways and best practices