AI Prompt Engineering for Developers

Course Overview

Prompting is not just “chatting”; it is a New Programming Paradigm. In this course, you will learn to design Deterministic Prompt Workflows, master Chain-of-Thought reasoningArcane GlossaryA prompting technique where an AI is asked to 'think step-by-step' to improve reasoning accuracy and reduce hallucinations., and build Prompt Templates that allow AI to perform specialized engineering tasks with 99% accuracy.

⚡ Instant QuickStart: The “Perfect Prompt”

Use this meta-prompt to get high-quality code from any model instantly.

Act as a Senior [Language] Architect. 
Task: [Your Task]
Constraints: No extra dependencies, use TDD, strict typing.
Format: Return ONLY the code + a brief verification plan.

---

Learning Objectives

• Master Structured Prompting: Context – Role – Task – Constraints.
• Implement Pattern-Based Prompting: Few-ShotArcane GlossaryDefinition not found in Grimoire., Chain-of-ThoughtArcane GlossaryA prompting technique where an AI is asked to 'think step-by-step' to improve reasoning accuracy and reduce hallucinations., and React.
• Design Prompt SOPs for automated code reviews.
• Identify and mitigate AI HallucinationsArcane GlossaryDefinition not found in Grimoire. through cross-verification prompts.

Technical Deep Dive: The Latent Space

Large Language Models represent knowledge in a High-Dimensional Latent Space.

• Temperature: Controls the “Randomness” (0.0 for deterministic code, 0.7+ for creative writing).
• System Prompts: The “Constitutional” layer that defines the model’s core personality and safety boundaries.
• Token Windows: Understanding how much context (past messages) the model can “remember” at once.

---

Walkthrough: The “Reviewer” Chain

Step 1: The Context Injection

Don’t just paste code. Paste the Context Schema.

# PROJECT CONTEXT
Framework: Astro 6
Style: Tailwind 4
Goal: High-performance landing page.

# TASK
Review the following component for A11y and performance.

Step 2: Few-Shot Examples (The Secret Sauce)

Give the model 2 examples of “Good” vs “Bad” outputs before asking for the real task.

Bad: "Add some styles."
Good: "Implemented flexbox centering with 2rem gap for mobile responsiveness."

Step 3: Chain-of-Thought (CoT) Ritual

Ask the model to “Think step-by-step” before providing the final answer. This reduces errors by force-creating a reasoning trace.

---

Pro Patterns: The Meta-Agent Ritual

Build a Prompt-Generating Prompt. Use a powerful model to write the instructions for a smaller, faster model. This optimizes for both quality and cost in high-volume agentic systems.

Capstone Project: The Automated PR Reviewer

Build a system that reviews code for your team.

1. Design a System Prompt that checks for 5 specific “Antipatterns”.
2. Create a script that feeds a git diff into the prompt.
3. Verify: The output should be a structured JSON report that can be automatically posted as a comment on GitHub.

---

The words are your code; the context is your compiler. Master the language of the machine.