Fourth Industrial Revolution

Summary : Retrieval-Augmented Generation (RAG) fixes one of the biggest issues of large language models: stale or hallucinated facts. This blog post explains five practical, surprising truths about RAG—how it updates knowledge without retraining, alternative architectures, prompt requirements, multimodal future, and the ecosystem that makes RAG practical for production. First, view the RAG Explained video. Then read on to learn how to design safer, more reliable LLM applications. Introduction Large language models are powerful but inherently static: their knowledge reflects only what was in their training data. That makes them prone to hallucinations and out-of-date answers. RAG gives an LLM access to current, verifiable information at query time, by retrieving relevant documents and using them to ground its responses. The RAG concept is simple, but the engineering choices and trade-offs are important. Below are five high-impact truths that change how you build and evaluate RAG sys...

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 : My new CodeCoach app is a Gemini-powered multimodal application that turns any coding session into instant understanding and career-ready assets. Paste code, upload a screenshot, or record a short voice note — CodeCoach explains your code, suggests improvements and tests, generates interview Questions and Answers, and produces polished resume, LinkedIn, and GitHub text you can use immediately. What is CodeCoach? CodeCoach helps developers, QA engineers, and data practitioners make their daily work visible. Instead of letting valuable fixes, refactors, and experiments disappear into commit history, CodeCoach creates concise technical explanations and ready-to-publish professional artifacts. It combines code understanding with real-world context so you can quickly communicate impact to hiring managers, teammates, and recruiters. View CodeCoach working in action  here . How it works, in a few seconds Use one of three simple inputs: Text : Paste a cod...