Prompt Engineering Course

Duration: 15 min

Prompt Engineering Course

Complete 15-module course on practical prompt engineering for LLMs.

Course Overview

Learn to design, test, and deploy effective prompts for ChatGPT, Claude, and other language models. Master techniques like few-shot prompting, chain-of-thought reasoning, RAG integration, and production patterns.

Total Duration: ~300 minutes (5 hours) Skill Level: Beginner to Intermediate Prerequisites: Basic familiarity with LLMs (ChatGPT, Claude)

Module List

| # | Module | Duration | Topics | |---|--------|----------|--------| | 1 | What is Prompt Engineering? | 18 min | Why it matters, how LLMs work, core principles | | 2 | Basic Prompting | 20 min | Clarity, specificity, format, boundaries | | 3 | Role & System Prompts | 22 min | Personas, behavioral guidelines, system vs user prompts | | 4 | Few-Shot Prompting | 21 min | Pattern matching, examples, consistency | | 5 | Chain-of-Thought | 23 min | Step-by-step reasoning, self-consistency | | 6 | Output Formatting | 19 min | JSON, CSV, markdown, structured extraction | | 7 | Prompt Templates | 18 min | Reusability, variables, f-strings, Jinja2 | | 8 | Working with Code | 22 min | Generation, debugging, refactoring, testing | | 9 | Summarization & Analysis | 20 min | Extractive vs abstractive, key points, sentiment | | 10 | RAG-Ready Prompts | 21 min | Context injection, grounding, citation, hallucination prevention | | 11 | Multi-Turn Conversations | 20 min | Context management, sliding window, summarization | | 12 | Prompt Injection Defense | 22 min | Attack patterns, input validation, boundaries, output filtering | | 13 | Evaluation & Testing | 20 min | Consistency, accuracy, cost analysis, A/B testing | | 14 | API Integration | 24 min | OpenAI/Anthropic SDKs, streaming, function calling, error handling | | 15 | Production Patterns | 25 min | Versioning, caching, fallbacks, cost optimization, monitoring |

Each Module Includes

✓ Real prompt examples with actual input/output ✓ Python code snippets for API integration ✓ 3 quiz questions with marked answers (✓) ✓ 120-200 lines of substantive content ✓ 15-25 min learning time per module

Key Takeaways

After completing this course, you'll be able to:

  • Write clear, specific prompts that produce reliable results
  • Use advanced techniques like few-shot, CoT, and RAG
  • Build production systems with caching, versioning, and fallbacks
  • Evaluate and optimize prompts systematically
  • Integrate LLM APIs securely and cost-effectively
  • Defend against prompt injection attacks

File Structure

prompt-engineering/
├── mod-1.md  (What is Prompt Engineering?)
├── mod-2.md  (Basic Prompting)
├── ...
├── mod-15.md (Production Patterns)
└── README.md (this file)

Getting Started

1. Start with Module 1 for foundational concepts 2. Work through modules 2-9 to master core techniques 3. Explore modules 10-12 for advanced patterns 4. Finish with 13-15 for production deployment

Each module builds on previous concepts but can be referenced independently.

Quick Navigation

  • Beginners: Start with modules 1-6 (basics + system prompts)
  • Developers: Jump to modules 7-8 (templates + code)
  • Production: Focus on modules 14-15 (API + patterns)
  • Security: See module 12 (injection defense)

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Created: June 2024 Format: Markdown (compatible with ailearningclub.com build system) Audience: Developers, product managers, content creators, researchers

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