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 : Learn five practical prompting techniques—role prompting, few-shot examples, chain-of-thought, guardrails, and self-consistency—that help you get precise, reliable, and safer outputs from AI tools like ChatGPT. Use these methods to turn generic answers into expert-level responses. Why prompts matter Great AI output starts with a great prompt. If your results feel generic or off-target, the issue is rarely the model; it is the input. Prompt engineering is the practical skill of crafting inputs so the model reliably produces the outcome you want. These five techniques will help you move from reactive questioning to deliberate instruction. View my  Prompt Engineering & Prompting Techniques video . Then read on. 1. Give the AI a job title: Role Prompting Assign a persona or role to the model to frame its tone, vocabulary, and depth. For instance, asking an AI to "act as a cybersecurity expert" leads to technical, risk-focused answers. Role prompting is a quic...

In this post, I explain Fine Tuning Large Language Models: Fine Tuning, Transfer Learning, Pretraining vs Fine-Tuning, Dataset Curation, Classification, Generation, Entity Matching, Sequence Instructioning), Annotation, Labeling Strategies & Synthetic Data for Domain Adaptation, Fine-Tuning Workflows, Parameter-Efficient Fine-Tuning, Instruction Tuning & Sequential Instruction Fine-Tuning, RLHF, Reward Modeling, and Safety Tuning, Fine-Tuning for Specialized Use Cases: Domain Adaptation & Entity Matching, Adaptive Machine Translation, Model Architectures & Scaling Considerations for Fine-Tuning, Hyperparameters, Optimizers & Practical Recipes (LR, Schedules, Batch Size), Mixed Precision, Memory Optimization, and Distributed Training. If you want my full Fine Tuning LLMs document also including the following topics, you can use the Contact Form (in the right pane) or message me in LinkedIn: Tooling & Frameworks, Offline Metrics, Human Evaluation, and Task-Speci...

In this post, I explain Introduction to Generative AI with Large Language Models, Key Concepts & Definitions, Underlying Models: Transformers & Beyond, Modeling andTraining Foundations, Sampling & Decoding for Generation Quality, Prompting Strategies for Generative AI (zero-shot, few-shot, chain-of-thought prompting, role prompting, and advanced prompt tactics), Scaling & Emergent Capabilities in Generation, Mitigating Hallucination & Ensuring Output Reliability -RAG and grounding, and Advanced Generation: Multimodality & Specialized Content. If you want my full Gen AI with LLMs document also including the following topics, you can use the Contact Form (in the right pane) or message me in LinkedIn:  Popular Generative LLMs & Frameworks (GPT-series, Claude, PaLM, Gemini, LLaMA), Efficiency & Deployment Optimization distillation, quantization, parameter-efficient tuning etc.), Ethics, Privacy & Governance, Generative AI Project Workflow (end-to-e...

In this post, I explain Confusion Matrix in detail. Learn Confusion Matrix Definition and Intuition, Claim Approval Example, Confusion Matrix Table Layout, Core Concepts Explained (TP, TN, FP, FN), Confusion Matrix Formulae, Derived Metrics from the Confusion Matrix (Precision, Recall, F1, Specificity), and Visualization and Code. If you want to additionally learn about the following confusion matrix topics or comment, you can do so on my original Confusion Matrix article on LinkedIn here . Thresholding, ROC and PR Curves, Imbalanced Data and the Accuracy Paradox, Multiclass and Multi-Label Confusion Matrices (Visualization and Interpretation), Cost-Sensitive Decisions: Cost Matrix, Business Tradeoffs, and Setting Operational Thresholds, Calibration, Confidence, and When to Trust Model Probabilities, Practical Tips and Troubleshooting (Data leakage, label noise, sampling effects) — confusion matrix tutorial, debugging checklist for AI Developers and AI QA Testers, Ethics, Fairness an...

In this post, I explain Introduction to RAG in LLMs (Large Language Models), RAG Concepts in LLMs, Retrieval Modules and Vector Embeddings, Indexing Strategies and Vector Databases, Document Ingestion and Preprocessing, RAG in LLM Python, RAG Frameworks (such as LangChain and LlamaIndex), Retrieve‑Then‑Generate vs Generate‑Then‑Retrieve, Prompt Engineering for RAG and Evaluation Metrics for RAG. You can test your knowledge of LLMs in Python by attempting the Quiz after every set of Questions and Answers. If you want my complete Retrieval-Augmented Generation (RAG) Framework in LLMs document that additionally includes the following important topics, you can message me on LinkedIn : Optimization and Caching, Advanced RAG Techniques (such as RAG multimodal retrieval), RAG in LLamaIndex Example with code, Best Practices and Troubleshooting RAG and RAG in LLM consolidated Quiz with multiple‑choice questions and answers to test your knowledge. Question : What does RAG stand for in...