Fourth Industrial Revolution

In this post, I explain LLMs in Python, Python Setup & Installation, Inference with Transformers, Calling ChatGPT API in Python, Python Local Deployment with Hugging Face Models, Prompt Engineering in Python and FineTuning & Custom Training (including LoRA). 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 Introduction to LLMs in Python document that additionally includes the following important topics, you can message me on LinkedIn : Python Advanced Techniques (Streaming, Batching & Callbacks), Python Efficiency & #Optimization (quantization, distillation, and parameter‑efficient tuning), Integration & Deployment Workflows, LLMs in Python Best Practices & Troubleshooting, and consolidated Introduction to LLMs in Python Quiz (with answer explanations to reinforce learning). Question : What do I mean by "Introduction to LLMs in Python"? Answer : Introduction to LL...

In this post, I explain Introduction to Prompt Engineering for ChatGPT, Key Concepts and Prompt Types (such as zero-shot, few-shot, chain-of-thought prompting), Best Practices, Advanced Prompt Engineering Tactics, Prompt Engineering for Coding and Testing, Multi‑modal and Complex Prompts and Evaluating and Iterating Prompts. You can test your knowledge of Prompt Engineering by attempting the Quiz after every set of Questions and Answers. If you want my complete Prompt Engineering for ChatGPT document that additionally includes the following important topics, you can message me on LinkedIn : Prompt Engineering Tools and Frameworks (GitHub repositories, APIs), Ethics and Prompt Safety, Use Cases and Workflows and Interview Preparation and Prompt Engineering Quiz. Question : What is prompt engineering for ChatGPT? Answer : Prompt engineering for ChatGPT is the deliberate design and structuring of input text to guide the model’s behavior toward desired outputs. By crafting precise...