Prompt Engineering
Prompt engineering is the practice of crafting inputs that guide an AI model’s behavior and output, helping you get more accurate, relevant, and useful results. There are several techniques you can apply depending on your goal, whether it's improving reasoning, controlling output format, or increasing reliability.
In this article series, I explore the main Prompt Engineering techniques, with practical examples and best practices you can apply right away. Below, you’ll find all articles in the series, along with the prompt examples and best practices.
Articles in the Series
Zero-Shot, One-Shot, Few-Shot & In-Context Learning
https://henriquesd.com/articles/prompt-engineering-zero-shot-one-shot-few-shot-and-in-context-learningChain-of-Thought, Skeleton-of-Thought & Tree-of-Thought
https://henriquesd.com/articles/prompt-engineering-chain-of-thought-skeleton-of-thought-and-tree-of-thoughtSelf-Consistency, Direct Stimulus & ReAct
https://henriquesd.com/articles/prompt-engineering-self-consistency-direct-stimulus-and-react
What You’ll Learn
- How different prompting techniques influence AI responses
- When to use each technique for better results
- Practical patterns you can reuse in real-world scenarios
