Practical Prompt Engineering
Learn to communicate with AI models effectively — craft prompts that get reliable, high-quality outputs every time. Prompt engineering is the art and science of designing inputs to AI language models to get the best possible outputs.
Level: Beginner · Category: Tools & Frameworks · Estimated time: 4 hours
Lessons
- How LLMs Work & What Prompts Do — A mental model of large language models — tokens, context windows, temperature, and why prompt wording matters.
- Zero-Shot, One-Shot & Few-Shot Prompting — The spectrum from no examples to curated demonstrations — and when to use each.
- Chain-of-Thought Reasoning — Make LLMs show their work — dramatically improving accuracy on math, logic, and multi-step tasks.
- System Prompts, Personas & Instructions — Using system messages to define roles, set tone, impose constraints, and create consistent AI personas.
- Structured Outputs & Response Formatting — Force JSON, tables, markdown, or any schema — eliminating unpredictable free-form responses.
- ReAct, Self-Consistency & Tree-of-Thought — The frontier of prompting — interleaved reasoning and acting, majority voting, and exploring multiple reasoning paths.
- Prompt Evaluation & Refinement — Build a systematic workflow for improving prompts — metrics, red-teaming, A/B testing, and version control.
Topics covered
prompt-engineering, llm, chatgpt, ai-tools, few-shot, chain-of-thought, structured-outputs