Prompt Engineering for Engineers
Treat prompts as code. Learn the 6-part framework to get deterministic, production-grade results from LLMs.
If you are building software, you need a reliable data processor, not a chatbot. Most developers treat Prompt Engineering as a dark art - tweaking adjectives and hoping for the best. This is wrong. Prompt Engineering is like API design using natural language.
In this tutorial, we are going to kill the "vibes-based" approach and use a predefined template. We will take a vague, failing request and refactor it into an instruction set that returns a reliable JSON, every single time.
You are moving from "Can you please help me?" to function(context) -> output.
Tutorial Goals
- Master the 6-Part Framework for writing production-ready prompts
- Use delimiters to sandbox user input and prevent confusion
- Implement Few-Shot Prompting to force adherence to specific formats
- Use Chain of Thought (CoT) to force the model to think step-by-step
- Learn the Anti-Patterns that cause hallucinations and bad output formatting