Fourth Industrial Revolution

Summary : This is practical advice for building reliable LLM applications in Python. Learn secure secret handling, few-shot prompting, efficient fine-tuning (LoRA), quantization for local inference, and strategies to manage the model context window. First, view the 7-minute Intro to LLMs in Python video for explanations. Then read on. 1. Treat API keys like real secrets Never hard-code API keys in source files. Store keys in environment variables and load them at runtime. That keeps credentials out of your repository and reduces the risk of accidental leaks. Example commands: export OPENAI_API_KEY="your_key_here" # Linux / macOS set OPENAI_API_KEY="your_key_here" # Windows (Command Prompt) For production, use a secure secrets manager (Azure Key Vault, HashiCorp Vault) and avoid committing any credential material to version control. 2. Guide models without heavy fine-tuning: few-shot prompting You can shape an LLM's behavior by giving it examples i...

Summary : Context Engineering is the architecture that lets AI remember, personalize, and act reliably across sessions. Beyond crafting clever prompts, it assembles the right data, tools, and memory hygiene so AI systems behave like thoughtful personal assistants,  and not forgetful librarians. Beyond RAG: Why Most AI Forgets the Moment You Close the Chat We’ve all had the same experience: a helpful conversation with an AI assistant, then a fresh chat that treats us like a total stranger. Every interaction feels like the first. That friction isn’t just annoying, but it also exposes a core architectural limitation of many AI systems. By default, Large Language Models (LLMs) operate as essentially stateless systems. They reason inside a temporary "context window" that vanishes when the session ends. If you want an AI that remembers, learns, and personalizes over time, you must design for state. That’s what Context Engineering does: it builds the framework that transforms...