Prompt Engineering: How to Use LLMs (ChatGPT, Claude, Gemini, etc.) to Get What You Want

Module 1 – Foundations of LLMs

3Lessons ·
- How large language models work (tokenization, context windows, embeddings)
- Understanding model limits and biases
- Differences between GPT, Claude, Gemini, and open-source models
Module 2 – Crafting Effective Prompts

4Lessons ·
- Prompt structures: instruction, context, example, format
- Few-shot vs. zero-shot prompting
- Chain-of-thought reasoning and step-by-step scaffolding
- Techniques: role prompting, constraints, temperature, system messages
Module 3 – Advanced Prompting Techniques

4Lessons ·
- Multi-turn and multi-agent workflows
- Self-consistency and verification loops
- Data-to-text (CSV → insights), code-generation, and summarization use cases
- Retrieval-Augmented Generation (RAG) basics
Module 4 – Practical Applications

4Lessons ·
- Business & productivity use cases (marketing, reports, research)
- Software development & debugging with LLMs
- Data analysis, visualization, and automation via prompts
- Ethical use, privacy, and intellectual property considerations
Capstone Project

1Lessons ·
- Build a custom AI assistant or knowledge-based agent for a specific task (e.g., financial analysis or content creation)